JP4765274B2 - Speech synthesis apparatus and speech synthesis method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a voice synthesizer capable of reading out a user registration memorandum, storage folder name, information during recording, reproduction frequency or favorable public image information which are information incidental to image or acoustic data, generating a sentence based on the read-out information, subjecting the generated sentence to voice synthesis, and outputting the same as voice. <P>SOLUTION: An information read-out section 2 reads out the information incidental to the image or acoustic data. A sentence generation section 3 generates the sentence based on the information read out by the information read-out section 2. A text voice synthesis section 4 performs voice synthesis of the sentence generated by the sentence generation section 3 and outputs the same. <P>COPYRIGHT: (C)2006,JPO&amp;NCIPI

Description

  The present invention relates to a speech synthesizer and a speech synthesizer, and more particularly to a speech synthesizer and a speech synthesizer that synthesize a speech based on information attached to an image or sound data.

  In recent years, digital still cameras, personal digital assistants (PDAs), etc. record image files such as images, moving images and music files and files related to images in a removable memory, which is a removable memory, based on the digital camera file format, Files have been easily exchanged between the above devices and related devices such as printers.

  For example, when an image file shot with a digital still camera and recorded on a removable medium based on the digital camera format is to be called up by a related device or the like, an image search is performed. An image search is also performed when a digital still camera tries to recall a target image file at a later date.

  In general, in an image search method and an image search apparatus, a search key registered in an image information search database corresponding to an original image is used, or reduced images for headline search are arranged and displayed, and focus selection is performed. The display and appreciation of the original image using the link structure between the reduced image and the original image is performed in various forms according to the following patent documents.

  Patent Document 1 discloses a matrix display technique of reduced headline images and attached data used for a presentation apparatus. Patent Document 2 discloses a technique for recording and reproducing a header image when used in a medium recording / reproducing apparatus for electronic images. Japanese Patent Application Laid-Open No. 2004-228561 discloses a technique for giving a frame focus display to a reduced image selected by an electronic image display device to notify a selection state display. Patent Document 4 discloses a technique for registering additional attribute information in searched image data. Patent Document 5 discloses a technique for selecting one of the reduced images displayed in an array and displaying the subsequent reduced image set in an array starting from the selected one. Patent Document 6 discloses an example of a system in which there is a still image moving image search device that searches for images using arrayed header reduced images. Patent Document 7 discloses an information service receiving apparatus and an information service receiving method. Patent Document 8 discloses a technique for assigning an identification name of a “digital camera” memory card. Further, Non-Patent Document 1 discloses that operability is greatly improved if a database can be searched for a plurality of items and data necessary for searching the database with natural language sentence expressions close to everyday conversation.

JP-A-61-235981 JP-A-62-248389 Japanese Patent No. 2530612 Japanese Patent No. 03192613 Japanese Patent Laid-Open No. 02-90876 Japanese Patent No. 3081304 JP 10-91499 A JP 2001-169225 A "Hitachi review" VOL. 69, no. 3 (1987-3), pages 23-27, "Natural Language Interface for Computers"

  However, as described above, when browsing a search image on a miniaturized portable image display device, the size of the display screen attached to the device is further miniaturized. It has become very difficult to recognize and discriminate. Further, even when trying to display the registration key, the display screen itself is small, so that the characters cannot be enlarged, and a large number of characters cannot be displayed at the same time, making it difficult to accurately select the desired image.

  In addition, the small portable image display device uses a media file format dedicated to the small portable image display device because of its circulation with the personal computer PC of the recorded contents.

  The present invention has been made in view of the above circumstances, and reads out and reads out user registration memos, storage folder names, information at the time of recording, reproduction frequency, or favorable information, which are information accompanying image or sound data. An object is to provide a speech synthesis apparatus and a speech synthesis method that generate a sentence based on the information, and synthesize the voice of the generated sentence and output the voice as a voice so that the user can search for an image by voice synthesis. To do.

In order to solve the above problem, a speech synthesizer according to the present invention includes an information reading unit that reads information associated with an image or sound data, and a sentence that generates a sentence based on the information read by the information reading unit. a generation unit, a text generated by said sentence generating means possess a voice synthesizing means for outputting the speech synthesis, the sentence generation means reads by said information reading means to determine the content of the sentence to be output Storing a character string analyzing means for analyzing a character string based on the outputted information, a word extracting means for extracting a word from the character string analyzed by the character string analyzing means, and a template used for generating a sentence. A template storage means, wherein the sentence generation means receives a character string including the word extracted by the word extraction means from the template storage means. -Option template is deformed to fit the rules for sentence structure determination requesting, a string the deformation generates a sentence by applying the above template.

  The information reading unit extracts necessary information from the image or the sound data, the sentence generation unit generates a guide sentence based on the information, the text-to-speech synthesis unit synthesizes the generated guide sentence, and outputs the sound. .

In order to solve the above problems, the speech synthesis method according to the present invention includes an information reading step for reading information associated with an image or sound data, and a sentence for generating a sentence based on the information read by the information reading step. a generation step, a text generated by the text generation step possess a speech synthesis step to output the speech synthesis, the sentence generating step reads by the information reading step in order to determine the contents of the sentence to be output A character string analyzing step for analyzing a character string based on the information that has been issued, a word extracting step for extracting a word from the character string analyzed in the character string analyzing step, and a template used for generating a sentence are stored. A template selection step of selecting a template from the template storage means, and in the sentence generation step, the template is extracted by the word extraction step. A string containing the word, deform to fit the rules for sentence structure determination template selected from the template storage means for requesting a character string described above modified to produce a sentence by applying the above template.

  The speech synthesizer and the speech synthesis method of the present invention read out user registration memos, storage folder names, information at the time of recording, reproduction frequency, or preference information, which are information accompanying image or sound data, A sentence can be generated based on the generated sentence, and the generated sentence can be synthesized as a voice and output as a voice. Therefore, by applying the speech synthesizer of the present invention to a portable video / audio recording / reproducing apparatus, it is possible to search for an image recorded / reproduced by the audio / video recording / reproducing apparatus based on information guided by voice. .

  When the speech synthesizer and speech synthesis method of the present invention are applied to a portable video / audio recording / reproducing device, the following effects can be obtained. First, there is no need to separately record audio data files of explanation audio for image explanation or music file correspondence explanation, and even if the number of shots is increased by several digits, for example, thousands or more, a corresponding explanation audio audio file can be created. It is possible to output a guide sentence voice of a file description by text voice synthesis without recording.

  In addition, when it takes time to seek on a medium such as an optical disk as a writing medium, control for searching the audio file of the corresponding explanation voice by the file system is not necessary, and even if the number of files increases, the explanation voice can be obtained. Access time does not increase.

  In addition, in a state where the selected display is additionally displayed on the reduced image, the image additional information code corresponding to the selected reduced image is output to the text-to-speech synthesis interface means, so that the display screen of the portable device is further reduced. Even if it is difficult to recognize the contents of the reduced image, the individual additional reduced image is selected, so that the additional image information corresponding to the selected reduced image can be recognized as the explanation voice.

  In addition, since there is no need to record a separate audio explanation audio file, the number of times of writing is reduced, so there is a limit on the number of times of writing, or writing / erasing all at once, such as a memory card using flash memory The media can be used even longer.

  Also, since it is based on speech synthesis, it is possible to hear and grasp information without needing to omit information without hiding a small screen or obstructing display by displaying characters.

  It can handle multiple languages. It can handle multiple user ages. It can support multiple playback locations. It can also handle multiple recording locations.

  Hereinafter, the best mode for carrying out the present invention will be described. This embodiment is widely used as an interchangeable media file format (Exchangeable Image File Formqt: Exif), which is widely used as a portable photographic image media format. Audio applied to a portable video / audio recording / reproducing apparatus that records a photographed image or sound on a removable medium using a DCF (Design Rule for Camera File) system and reproduces the photographed image or sound from the removable medium. It is a synthesizer.

  The voice synthesizer generates a sentence using information described in the photographic media format recorded on the removable medium by the portable video / audio recording / playback apparatus according to the DCF system, and synthesizes and outputs the generated sentence. The information described in the photographic media format here refers to information accompanying the image or sound data, such as a user registration memo, the name of the storage folder for storing the image data, the information at the time of recording, the reproduction frequency, or the preference. There is sensitivity information.

  FIG. 1 is a block diagram of the speech synthesizer 1. The speech synthesizer 1 includes an information reading unit 2 that reads information attached to an image or sound data, a sentence generation unit 3 that generates a sentence based on the information read by the information reading unit 2, and a sentence generation unit 3 And a text-to-speech synthesizer 4 for synthesizing and outputting the sentence generated by.

  FIG. 2 is a flowchart showing an overall processing procedure of the speech synthesizer. The information reading unit 2 extracts necessary information from the image or the sound data (step S1), the sentence generation unit generates a guide sentence based on the information (step S2), and the generated guide sentence is converted into a text-to-speech synthesis unit. Synthesizes speech (step S3) and outputs speech.

  As described above, the information reading unit 2 reads information attached to the image or sound data including, for example, a user registration memo, a storage folder name, information at the time of recording, reproduction frequency, or preference information. Hereinafter, reading of the accompanying information of the information reading unit 2 will be described. FIG. 3 is an image file configuration example of an Exif file that is a replaceable media image file format. FIG. 4 shows an example of the audio file structure of an Exif file.

  In the image file configuration example of the Exif file, as shown in FIG. 3, the beginning of the JPEG stream is indicated by SOI (Start Of Image). APP1 (Application marker segment 1) is inserted immediately after SOI. APP1 contains Exif information. Details of this Exif information will be described later. A JPEG table is described following APP1. Quantization tables, Huffman tables, frame heads, scan heads and the like are included. Next, JPEG basic original image data follows. The end of JPEG data is indicated by EOI (End OF Image).

  The Exif information described in APP1 includes an APP1 marker, an APP1 data length, an Exif identification code (ExifIFD), and an attached information body. The size of APP1, which includes all of these, does not exceed 64kByte according to the JPEG standard. Attached information can record up to two IFDs (0thIFD, 1stIFD). In 0thIFD, auxiliary information regarding the compressed image (main image) is recorded. In 1stIFD, thumbnail images (JPEG compressed basic thumbnail data) are recorded.

  ExifIFD is a collection of tags for recording additional information unique to Exif. It consists of Exif version tag, image data characteristic tag, image structure tag, user comment tag, related file information tag, date / time tag, shooting condition tag, and IFD pointer compatibility.

  The version tag indicates the version supported by this Exif standard. If this field does not exist, it is determined that the field does not conform to this Exif standard. When conforming to this Exif standard, 4 bytes of ASCII “0210” is recorded.

  The image data characteristic tag is information indicating a color space. In this standard, sRGB (= 1) that defines the color space from the observation conditions on the PC monitor is used.

  The image structure tag includes an effective image width (PixelXDimension), an effective image height (PixelYDimension), a meaning of each component (ComponentsConfiguration), and an image compression mode (CompressedBitsPerPixel). The effective image width is information unique to the compressed data. When a compressed file is recorded, a significant image width is always recorded in the effective image width tag regardless of the presence of padding data and the presence of a restart marker. Do not write in uncompressed files. The effective image height is also information unique to the compressed data. When a compressed file is recorded, a significant image height is always recorded in the effective image height tag regardless of the presence of padding data and the presence of a restart marker. Do not write in uncompressed files. The meaning of each component is also information unique to the compressed data. The channel of each component is shown in order from the first component to the fourth component. The compressed image mode is also information unique to the compressed data. The compression mode set at the time of image compression is indicated by unit bit / pel.

  The user comment tag constitutes a tag related to user information together with the manufacturer note. The manufacturer note is a tag for the Exif writer manufacturer to enter individual information. The contents are left to the operation of each manufacturer. The user comment tag is a tag for an Exif user to write a keyword or comment as a user registration memo for an image in addition to the image description.

  In order to determine the character code written in the user comment, the character code type is entered with a fixed 8 bytes at the beginning of the tag data area, and the remaining area is padded with null. Character codes include ASCII, JIS, Unicode, and Undefined. References include ITU-TT.50IA5, JISX0208-1990, Unicode Standard, and Undefined. Exif readers always have the ability to distinguish character codes. And it displays according to the ability of the leader. Following the character code type, a user comment data field is provided. The Exif user writes a keyword or comment as a user registration memory by the character code identified by the character code type.

  The related file information tag records an audio file name related to the image data. As related information, record only the file name and extension of the Exif audio file that is the destination. There are three types of correspondence between Exif image files and Exif audio files. One Exif image file corresponds to one Exif image file, one Exif image file corresponds to one Exif image file, and one Exif image file corresponds to one Exif image file Corresponding relationship. When an image file and an audio file are associated with each other using this tag, the related information must be described on the associated audio file side.

  The tags related to the date and time are the original image generation date and time DateTimeOriginal, digital data creation date and time DateTimeDigitized, DateTime subsec SubsecTime, DateTimeOriginal subsec SubsecTimeOriginal, and DateTimeDigitized subsec SubsecTimeDigitized.

  The original image generation date and time DateTimeOriginal indicates the date and time when the original image was generated. In the digital still camera, the date and time when the image was taken is recorded. The format is “YYYY: MM: DD HH: MM: SS”. The time is displayed 24 hours, and a space character [20.H] is filled between the date and the time. The length of the character string is 20 bytes including NULL. When there is no description, it is treated as unknown.

  Digital data creation date and time DateTimeDigitized indicates the date and time when the image was digitized. For example, when an image is captured by a digital still camera and a file is recorded at the same time, the original image generation date / time DateTimeOriginal and the digital data creation date / time DateTimeDigitize have the same contents. The format is “YYYY: MM: DD HH: MM: SS”. The time is displayed 24 hours, and a space character [20.H] is filled between the date and the time. The length of the character string is 20 bytes including NULL. When there is no description, it is treated as unknown.

  A sub-sec SubsecTime of DateTime is a tag for recording the time to the second unit after the decimal point in relation to the DateTime tag. The sub-sec SubsecTimeOriginal of DateTimeOriginal is a tag for recording the time to the second unit after the decimal point in relation to the generation time DateTimeOriginal tag of the original image. A sub-sec SubsecTimeDigitized of DateTimeDigitized is a tag for recording the time to the second of the decimal point in relation to the digital data creation date / time DateTimeDigitized tag.

  Tags related to shooting conditions are exposure time ExposureTime, shutter speed ShutterSpeedValue, aperture value ApertureValue, brightness value BrightnessValue, exposure correction value ExposureBiasValue, lens minimum F value MaxApertureValue, subject distance SubjectDistance, metering method MeteringMode, light source LightSource, Flash Flash, lens focal length FocalLength, F number FNumber, exposure program ExposureProgram, spectral sensitivity SpectralSensitivity, ISO speed rate ISOSpeedRatings, photoelectric conversion function OECF, flash intensity FlashEnergy, spatial frequency response SpatialFrequencyResponse, focal plane width resolution FocalPlaneXResolution, focal plane height resolution FocalPlaneYResolution, focus There are a surface resolution unit FocalPlaneResolutionUnit, a subject position SubjectLocation, an exposure index ExposureIndex, a sensor method SensingMethod, a file source FileSource, a scene type SceneType, and a CFA pattern CFAPattern. IFD pointer compatibility indicates a pointer to the compatibility IFD.

  The audio file configuration example of the Exif file has a data structure as shown in FIG. In the audio file, the file format for partitioning the storage area calls each data partition with the partition name “chunk”. The first “RIFF” chunk ID (ckID) is followed by the RIFF size specification (ckSize), followed by the “WAVE” type specification, the MPEG tag and stereo specification in the format chunk, and WAVE MP3Audio is registered. There is audio registration information data determined to be.

  Next, there is a fact chunk, followed by an INFO list chunk and an Exif list chunk as attached information sub-chunks. After that, the audio stream body data of the MPEG Layer3 compression format WAVE follows with the ASCII character string “Data” (64617461) as the chunk ID (ckID) and the start marker.

  Each chunk of INFO list chunk, Exif list chunk, and data chunk will be described. The INFO list chunk is alphanumeric, including title alphanumeric, genre alphanumeric, file creation date alphanumeric, comment alphanumeric, artist alphanumeric, and copyright alphanumeric.

  The Exif list chunk starts with a LIST marker, and describes its size, “exif” and its standard version. If there is an associated image file, its file name (pointer is not allowed) is “file name.extension”. ”Format, followed by year: month: day: hour: minute: second and second. It is described up to the subsecond in the subsecond description method.

  After that, there is a data field for user comment eucm. In this user comment, the eucm tag character is followed by the eucm chunk size, followed by the user comment which is chunk data. This user comment has a type declaration in uppercase ASCII with a character code of the first 8 bytes, preferably Unicode, and Undefined is specified when ShiftJIS is used in the Japanese personal computer situation. In this user comment column, an artist, a song name, a file creation date and time, a lyrics of a rust portion, etc. are registered as a character string as a 2-byte code character. This is used as user registration memo information in the present embodiment.

  The information reading unit 2 holds a correspondence table between the image file or audio file as described above and information necessary for speech synthesis. FIG. 5 is a specific example of a correspondence table for reading information.

  The data label of information necessary for composition, the pointer of where to refer to the Exif file, and the expression format in the Exif file are associated. For example, the storage folder name refers to “character string” from the Exif list chunk. For the file name, refer to “file name.extension” from the Exif list chunk. Refer to "Year: Month: Day: Hour: Minute: Second" from the Exif list chunk for the shooting time. Refer to the “character string” from the user comment eucm for the shooting location. For the reproduction frequency, reference is made to “number” from the user comment eucm. For the preference information, “character string” is referred from the user comment eucm. The user registration memo refers to “character string” from the user comment eucm. Then, in the information reading unit, using the correspondence table, each information data of the file attribute data of the present embodiment is referred from the location of the file, converted into a character string, and read.

  FIG. 6 shows an example of data read by the information reading unit 2. The information reading unit 2 uses the correspondence table for reading out the information above, from the storage folder name “family / dog /”, from the file name “peach walk.jpg”, from the shooting time “2002/06 / 27 ”and“ 09:52:15 ”are read from the shooting location by referring to“ Neighborhood Park ”and converting each into a character string. Also, 0 is read from the reproduction frequency, 5 is read from the preference information, and “Momo-chan went for a walk for the first time” from the user registration memo, and each is converted into a character string and read. The data shown in FIG. 6 read by the information reading unit 2 is sent to the sentence generation unit 3.

  FIG. 7 is a configuration diagram of the sentence generation unit 3. The sentence generation unit 3 includes an output content determination unit 5 that determines the output content and a sentence output unit 6 that generates a sentence that matches the output content determined by the output content determination unit 5. With this configuration, the sentence generation unit 3 determines the output language, sentence contents, and the number of sentences in the output content determination unit 5 based on the data shown in FIG. The sentence output unit 6 generates a sentence based on the template.

  FIG. 8 is a configuration diagram of the output content determination unit 5. The output content determination unit 5 includes a character string analysis unit 7a that analyzes a character string in order to determine the content of a sentence to be generated based on information obtained from data as shown in FIG. A word extraction unit 7b that extracts words from the character string analyzed in 7a, an output language determination unit 10 that determines an output language, a sentence content determination unit 11 that determines sentence content, and a sentence that determines the number of sentences Number determination unit 12 and a template selection unit 13 for selecting a suitable template from the template database 14.

  The character string analysis unit 7a gives linguistic information such as part of speech information to the data shown in FIG. 6 read by the information reading unit 2. Since the character string is divided into words in the information reading unit 2, the character string analysis unit 7 a can obtain linguistic information by comparing each word with the rule file 8 and the dictionary file 9. it can.

  The word extraction unit 7b extracts a word from the character string analyzed by the character string analysis unit 7a. Here, the extracted words are passed to the output language determination unit 10, the sentence content determination unit 11, and the sentence number determination unit 12.

  The output language determination unit 10 determines the type of language to be output. For example, the output language determination unit 10 assigns priorities in the order of user designation, language mode when the image and sound recording apparatus is activated, and user registration memo language. Of course, the output language determination unit 10 has a sub-module for language determination. The language determination submodule receives a character string and determines how many words the character string is.

  For language determination, any language determination method used in other natural language processing applications may be used. For example, the determination can be made based on the character code and character type of the input character string, and the characteristics of the arrangement of the character strings may be rules for determining the language. It is also possible to determine the language by having dictionary data inside and searching the input character string with a dictionary.

  The sentence content determination unit 11 determines what kind of sentence is generated as the guide sentence. When generating a sentence based on user registration memo, file name, shooting time, shooting location data, character string analyzed by the character string analysis unit 7a or word data obtained from the word extraction unit 7b Determine if the statement is about information. Further, the sentence content determination unit 11 determines whether or not to generate a guide sentence related to how a user perceives an image based on reproduction information, likability information, sensitivity key information, access frequency key information, and the like. When it is determined to generate a guide sentence related to the user's perception, control of text-to-speech synthesis is performed. Moreover, you may make it determine whether a greeting is produced with respect to a user based on application use time zone information. Of course, the content of the sentence may be determined based on the user designation information.

  The number-of-sentences determination unit 12 uses the user registration memo, file name, shooting time, and shooting location data in the character string analyzed by the character string analysis unit 7a or based on the word data obtained from the word extraction unit 7b. Determine the number of sentences. If the image recording mode information is known from the file name etc., it can be determined whether or not it is the continuous slide show mode, and further, it can be determined how many images are continuous from the sub-second tag of the ExifIFD date tag. In response, the fraction of the sentence to be output can be determined. Further, the sentence number determination unit 12 may add a sentence in accordance with the number of preference information.

  The template selection unit 13 selects language templates according to the instructions from the output language determination unit 10 for the number of sentences specified by the sentence number determination unit 12 with the contents determined by the sentence content determination unit 11. To do. If the sentence to be created is a sentence based on the user registration memo, the template selection unit 13 selects a template for the user registration memo. If a sentence about the shooting date is generated, a template for that is selected.

  Moreover, the template selection part 13 can also select a template according to the preference information by a preference key. For example, it is assumed that two types of data “this is <noun>” and “this is a favorite <noun>” are recorded in the template database 14. If the likability key indicates that likability for the image is good, the template selection unit 13 selects the template “This is a favorite <noun>”. If the preference is not good, the other template is selected.

  Further, the template selection unit 13 can select a template based on information indicating what user is using the application. For example, when the user is a teenager, the template selection unit 13 can select a template with a simple tone such as “This is <noun>.”

  FIG. 9 is a flowchart showing a processing procedure of the output content determination unit 5. First, in brief description, a character string is analyzed by the character string analyzing unit 7a, a word is extracted by the word extracting unit 7b (step S11), and an output language is determined by the output language determining unit 10 (step S12). The sentence content determination unit 11 determines the sentence content (step S13), the sentence number determination unit 12 determines the number of sentences (step S14), and the template selection unit 13 selects a template (step S15). Each template is sent to the sentence output unit 6 until YES is determined by determining whether or not templates have been selected for the number of sentences to be output (step S16). The sentence output unit 6 generates a sentence based on each template.

  Below, the processing procedure of the output content determination part 5 is demonstrated in detail. The character string analysis unit 7a analyzes the character string in step S1 and adds information necessary for subsequent processing. In the present embodiment, it is assumed that the analysis content of the character string analysis unit 7a is a morpheme analysis for obtaining a morpheme boundary and a part of speech of each morpheme for an input character string. For this reason, the character string analysis unit 7a uses the rule file 8 and the dictionary file 9 for analysis.

  The rule file 8 contains rules related to the morpheme arrangement (grammar, connection matrix, morpheme N-gram, etc.) and rules for assigning necessary information to morphemes not registered in the dictionary. The dictionary file 9 stores information on registered morphemes such as notation, part of speech, and reading.

  The morpheme boundary and part-of-speech can be determined using an empirical method such as the longest match method or the minimum number of division method, or the analysis can be performed using grammatical connectivity or a statistical language model. You can do it. The method of morphological analysis is not particularly specified in the present embodiment. For example, a free morphological analysis system tea bowl (http://chasen.aist-nara.ac.jp/hiki/ChaSen/) is used. It can be used as it is. For those having numerical values such as shooting time and favorable sensitivity information, the numerical values are extracted according to the format of FIG.

  As a result of the analysis of the character string by the character string analysis unit 7a, the data of FIG. 6 is output with the analysis result added as shown in FIG. That is, the analysis result of “family” − [noun] and “dog” − [noun] is added to the storage folder name “family / dog /” and output. In addition, an analysis result “peach”-[noun], “no”-[case particle], and “walk”-[noun] is added to the file name “peach walk.jpg” and output. The file name extension is analyzed as “jpg”. The shooting time “2002/06/27” is analyzed as “2002”, “06”, “27”, and “09:52:15” is analyzed as “09”, “52”, “15”. Is taken out. In addition, an analysis result of “neighborhood”-[noun], “no”-[case particle], and “park”-[noun] is added to the “neighborhood park” of the shooting location and output. Also, “0” is extracted from the reproduction frequency “0”, and “5” is extracted from the preference information “5”. In addition, the user registration memo “Momo-chan went for a walk for the first time” has “Momo”-[noun], “Chan”-[suffix], “ga”-[case particle], “first time”- [Adverb], “walk”-[noun], “ni”-[case particle], “line”-[verb], “tsu”-[suffix], “ta”-[auxiliary verb], “.”-[ The analysis result “Punctuation” is added and output.

  In step S11, the word extraction unit 7b extracts words from the analysis result of FIG. Here, the word extracted by the word extraction unit 7 b is applied to the template selected by the template selection unit 13 from the template database 14.

  Next, in step S12, the output language determination unit 10 determines how many sentences to generate. The output language determination unit 10 checks whether or not a value is entered in the user registration memo column of the data (FIG. 10). If a value is entered in the user registration memo column, the language of the user registration memo is set as the output language. If no value is entered in the user registration memo field, a default language is determined in advance and the default language is set as the output language. In the present embodiment, the description will be made assuming that the default language is Japanese.

  When there is a user registration memo, it is necessary to determine in which language the user registration memo is written. In this embodiment, the output language determination unit 10 holds a plurality of dictionaries for each language, counts the number of vocabulary of user registration memos in each language, and counts the most number of words. Determine the language of the dictionary that was used as the output language.

  Next, in step S13, the sentence content determination unit 11 determines what kind of content is to be generated. The sentence content determination unit 11 determines which of the data obtained from the word extraction unit 7b is used to generate a sentence, and which is used for control information for text-to-speech synthesis. Keep information. In this embodiment, sentence generation is performed based on the user registration memo, file name, shooting time, and shooting location data, and the text voice based on which information is related to the reproduction information and preference information. Assume that synthesis control is performed.

  The sentence content determination unit 11 checks the presence / absence of a value for each data label name (storage folder name, file name, file name extension,...). If the data used for sentence generation has a value, the information and actual value are output to the template selection unit 13.

  Next, in step S14, the sentence number determination unit 12 determines the number of sentences for each content. The default number of sentences is set to 1, and the number of sentences to be output is changed only when it matches the rule for determining the number of sentences. As the rule for determining the number of sentences, the user registration memo outputs the sentence of the number of sentences, adds a comment sentence when the preference information is 5 or more, and continues when the image is taken in continuous shooting mode. For example, a sentence of the number of copies is generated. This rule may be freely determined by the application designer.

  In the data of the present embodiment, since the user registration memo is composed of only one sentence, the sentence related to the user registration memo is one sentence, but since the preference information is 5, one comment sentence is added.

  Next, in step S <b> 15, the template selection unit 13 selects the number of sentences specified by the sentence number determination unit 12 according to the content determined by the sentence content determination unit 11. The language of the template follows the instruction from the output language determination unit 10.

  FIG. 11 is a specific example of a template used in this embodiment. In the present embodiment, the extension jpg indicating the image file, the shooting time, and the shooting location are described in the input data. Therefore, the template selection unit 13 reads “This image is <year>” from the template database 14. Was taken at <location> on <monthday>. Next, since there is a user registration memo, a template “<user registration memo>” is selected. In addition, one comment sentence is to be added, and since the favorable feeling is high, a positive one is selected from among the comment sentences, and “Nice image” is selected. The value enclosed in <> is substituted from the data in the subsequent processing. If there are multiple items listed in <> separated by ",", these are the restrictions for applying the template. If all items are not satisfied, the template cannot be applied.

  The output content determination unit 5 outputs the template selected by the template selection unit 13 to the sentence output unit 6.

  Next, the sentence output unit 6 will be described. FIG. 12 is a configuration diagram of the sentence output unit 6. The sentence output unit 6 includes a word deforming unit 15 that deforms a word string that is an input character string from the output content determining unit 5, a sentence structure determining unit 17 that determines the structure of the input sentence, and the data shown in FIG. And a template application unit 18 that extracts necessary values and applies them to the template.

  When applying the character string obtained by extracting the word extraction unit 7b from the data output in FIG. 10 to the template obtained from the output content determination unit 5, the word transformation unit 15 applies to the template. Transform the input string. At this time, the word deforming unit 15 may refer to the concept dictionary (thesaurus) file 16 and replace the input character string with another word. That is, the word deforming unit 15 transforms the word extracted by the word extracting unit 7b so as to apply to the template, or replaces it with another word obtained by referring to the concept dictionary file 16. A concept dictionary is a dictionary that represents a conceptual relationship between words. It is used to replace a character string with a word having the same concept or a word having a superordinate concept.

  For example, it is assumed that the template “<user registration memo>, <sentence>” is selected by the template selection unit 13. This template requires that the user registration memo has a sentence structure.

  Therefore, the sentence structure determination unit 17 checks whether the user registration memo is a sentence. The sentence structure determining unit 17 has the rules for determining the sentence structure shown in FIG. 13, and can check what structure the input has. In the rule of the present embodiment, if the input morpheme sequence matches the right side of the rule, it can be substituted with the symbol on the left side. Substituting to the end, the obtained symbol becomes the input structure. In this case, the input is determined as a sentence.

  In FIG. 13, if the input morpheme is “noun” or “noun suffix”, it can be substituted by [noun phrase 1]. If it is “noun each particle” or “noun suffix each particle”, it can be substituted by [noun phrase 2]. Also, “noun phrase 1”, “noun phrase 2”, “noun phrase 2 noun phrase 1”, “noun phrase 2 noun phrase 2”, “noun phrase 2 noun phrase 2 noun phrase 1”, “noun phrase 2 noun phrase 2 Noun phrase 2 ”can be substituted into [Noun phrase]. If the input morpheme is “verb”, “verb ending”, “verb auxiliary verb”, or “verb ending auxiliary verb”, it can be substituted with [verb phrase]. If it is “adverb”, it can be substituted with [modifier]. And if it is "noun phrase verb phrase", "noun phrase auxiliary verb", "modifier phrase, noun phrase verb phrase", "noun phrase modifier phrase, verb phrase", "noun phrase, modifier phrase noun phrase verb phrase" Can be substituted with [Statement].

  If it is determined that the user registration memo is a noun phrase, the template “<user registration memo>, <sentence>” cannot be used, so the character string of the user registration memo needs to be transformed. For example, if the content of the user registration memo is “Momo-chan's walk”, it can be made a sentence by adding “Is.” At the end, and a template can be applied. Further, in the case of the template “This is a favorite <file name>, <noun phrase>.”, The character string “Momo no Muro” is a noun phrase, so the word transformation unit 15 does nothing in particular.

  Also, when a template such as “This is <“ Flow ”> of <“ Color ”>” is selected, the concept dictionary is used to apply the template. In the above template, a character string surrounded by “” indicates that it is a concept. A noun representing color can be applied to the first part of the template. The second part contains words with the concept of flowers.

For example, it is assumed that the character strings obtained from the user registration memo are “red” and “tulip”. Information indicating that “red” and “tulip” are both nouns is obtained by the character string analysis unit 7 a of the output content determination unit 5. Further, when the concept dictionary file 16 is used, it is found that the red superordinate concept is color and the tulip superordinate concept is flower .

  By using these analysis results, it is determined that the character strings “red” and “tulip” can be applied to the template. As a result, the sentence “This is a red tulip” is generated.

  The template application unit 18 generates a sentence using the template selected by the template selection unit 13. Specifically, the template application unit 18 extracts a necessary value from the data shown in FIG. 10, or applies the word transformed or replaced by the word transformation unit 25 to the template. In the case of the template "This image was taken at <location> on <month> of <year>", the shooting date and shooting location are extracted from the data and applied to the template. The picture was taken in a nearby park on June 27, 2002. "

  Next, the text-to-speech synthesizer 4 will be described. The text-to-speech synthesis unit 4 is a module for converting the text generated by the sentence generation unit 3 into a speech waveform. FIG. 14 shows the configuration of the text-to-speech synthesizer 4. The text-to-speech synthesis unit 4 includes a language processing unit 21 for converting the input text into a phonetic symbol string, a prosody generation unit 24 for converting the phonetic symbol string from the language processing unit 21 into prosody data, and a prosody A waveform generation unit 27 for generating a speech waveform based on the prosodic data from the generation unit 24 is configured.

  The language processing unit 21 has a dictionary file 22 and a rule file 23, and converts the input text into a phonetic symbol string. The prosody generation unit 24 includes a rule file 25 and a data file 26, and converts the speech signal sequence from the language processing unit 21 into prosody data. The waveform generation unit 27 has a segment file 28 and generates a speech waveform based on the prosodic data from the prosody generation unit 24.

  Briefly explaining the operation, the text-to-speech synthesizer 4 converts the input text into a speech signal sequence by referring to the dictionary file 22 and the rule file 23 by the language processing unit 21, and the rule file by the prosody generation unit 24 25. The speech signal sequence is converted into prosodic data with reference to the data file 26, and a speech waveform is generated by the waveform generator 27 with reference to the segment database 28 based on the prosodic data.

  Hereinafter, detailed configurations and operations of the language processing unit 21, the prosody generation unit 24, and the waveform generation unit 27 will be described with reference to FIGS.

  FIG. 15 shows the configuration of the language processing unit 21. The language processing unit 21 is for changing the input text into a phonetic symbol string. The language processing unit 29 analyzes the input text and obtains information on each morpheme, and based on the morpheme information. And a prosodic information generation unit 30 for generating phonetic symbols.

  The text analysis unit 29 is connected to the rule file 30 and the dictionary file 31. The rule file 30 includes rules relating to morpheme arrangement (grammar, connection matrix, morpheme N-gram, etc.) and rules for assigning necessary information to morphemes not registered in the dictionary. The dictionary file 31 stores information on registered morphemes such as notation, part of speech, and reading.

  The text input to the text-to-speech synthesizer 4 is passed to the text analyzer 29 in the language processor 21. The input text may be a single sentence or a plurality of sentences, but in this embodiment, the description proceeds as a single sentence for ease of explanation.

  The input sentence is divided into morphemes by the text analysis unit 29 using the rule file 30 and the dictionary file 31, and information about each morpheme is obtained from the dictionary file 31. For morphemes that are not registered in the dictionary file 31, necessary morpheme information is generated using the rule file 30. The text analysis unit 29 in the language processing unit 21 of the text-to-speech synthesis unit 4 can use the same text analysis unit 7 as the output content determination unit 5 of the sentence generation unit 3. The morpheme information obtained in this way is sent to the prosodic information generation unit 32.

  The prosodic information generation unit 32 has a rule file 33 for analyzing morphological information and obtaining prosodic information. The rule file 33 stores rules for changing phrasing and reading based on morpheme information. Further, for each input morpheme, the dictionary file 31 used in the text analysis unit 29 is also connected to obtain more detailed information for prosody generation. The information for prosody generation is, for example, information on how reading changes when a plurality of morphemes are combined to form a compound word, information on the movement pattern of the accent nucleus position, etc. .

  In the present embodiment, the morpheme information and the prosody generation information are stored in one dictionary file 31, but each is stored as a separate dictionary file, and the text analysis unit 29 stores the morpheme information. A dictionary file storing only information is connected, and the prosody information generating unit 32 can perform an equivalent process with a configuration in which a dictionary file storing only information for prosody generation is connected. .

  The prosodic information generation unit 32 performs a process of adding prosodic information to the input sentence using a rule based on the morpheme information received from the text analysis unit 29. The prosodic information differs depending on the language, but in the case of Japanese, for example, the accent nucleus position, the accent strength, the pose position, the pose length, and the change in reading. Since the prosodic information differs depending on the design of the apparatus, it is not necessary to include all of the above information, and it is not necessary to limit to the above information.

  The prosodic information obtained by the processing in the prosodic information generation unit 32 is expressed by a phonetic symbol string. A phonetic symbol string is a representation of input text using phonetic symbols, accent symbols, pause symbols, tone symbols, and the like. As phonetic symbols and accent symbols, IPA (International Phonetic Alphabet) and SAMPA are widely known. As for pause symbols and tone symbols, ToBI (Tone and Break Indices) is famous. The language processing unit 21 outputs the obtained phonetic symbol string to the prosody generation unit 24.

  FIG. 16 shows the configuration of the prosody generation unit 24. The prosody generation unit 24 is for converting an inputted phonetic symbol string into prosodic data. The prosody generation unit 24 includes a duration determination unit 34 for determining the duration of each phoneme and a fundamental frequency determination unit 39 for determining the fundamental frequency of each phoneme.

  The duration determination unit 34 analyzes the speech signal sequence from the language processing unit 21 using the analysis data file 36 relating to phonemes, and the speech signal sequence analysis unit 35 analyzes the speech signal sequence analysis unit 35. A rule application unit 37 that determines a phoneme duration using a rule file 38 for determining the duration; The analysis data relating to phonemes stored in the analysis data file 36 indicates, for example, whether each phoneme is a vowel or the default duration of each phoneme. The rule for determining the duration length stored in the rule file 38 is a rule for determining in which phoneme environment the default duration length is to be expanded or contracted. The phonological environment refers to whether or not the phoneme is a vowel, what the preceding and following phonemes are, what number phoneme of the syllable, the accent nucleus position, and the like.

  The duration determination unit 34 analyzes the audio signal sequence from the language processing unit 21 using the analysis data file 36 in the audio signal sequence analysis unit 35, and uses the rule file 38 in the rule application unit 37. Determine the duration of phonemes. That is, information necessary for applying the rule by the rule applying unit 37 is obtained by analyzing the input phonetic symbol sequence by the audio signal sequence analyzing unit 35. Information on the duration time determined by the duration time determination unit 34 is sent to the fundamental frequency determination unit 39.

  The fundamental frequency determination unit 39 generates prosodic data from the speech signal sequence from the language processing unit 21 and the duration length information determined by the duration length determination unit 34. For this reason, the fundamental frequency determination unit 39 analyzes the fundamental frequency pattern of the speech signal sequence using the analysis data file 41 related to the fundamental frequency pattern, and obtains information necessary for rule application, Based on the information obtained by the audio signal sequence analysis unit 40, a rule application unit 42 that selects an optimum pattern using the pattern selection rule file 43, and an optimum pattern determined by the rule application unit 42 A time expansion / contraction unit 44 that deforms according to the duration of each phoneme already determined by the duration determination unit 34 and determines a fundamental frequency for each phoneme.

  Depending on the design of the device, there are various ways of realizing the analysis data holding method and the rule data selection method. In this embodiment, the accent type of the word to which the phoneme belongs, the strength of the accent, the dependency, etc. A method is used to determine the applicability of rules based on the sentence structure of

  The fundamental frequency pattern data holds a fundamental frequency value across a plurality of phonemes. In the analysis data file, patterns are classified for each prediction factor such as accent type and tone so that they can be selected by rules.

  The fundamental frequency determination unit 39 analyzes the input phonetic symbol string with reference to the analysis data file 41 by the voice signal sequence analysis unit 40, and obtains information necessary for rule application. Based on the obtained information, the rule application unit 42 refers to the pattern selection rule file 43 and selects an optimum pattern. The pattern selected by the rule application unit 42 is transformed by the time expansion / contraction unit 44 according to the duration of each phoneme already determined by the duration length determination unit 34, and the fundamental frequency for each phoneme is changed. It is determined. The duration length for each phoneme determined by the duration length determination unit 34 and the fundamental frequency for each phoneme determined by the fundamental frequency determination unit 39 are sent from the prosody generation unit 24 to the waveform generation unit 27 as prosody data.

  A part of the prosodic data is shown in FIGS. The first half of the prosody data is the duration data in FIG. 17, and the second half is the fundamental frequency data in FIG. In FIG. 17, the duration time data includes the leftmost sample number from the voice start point, the next phoneme string, and the rightmost duration length for each phoneme. In FIG. 18, the fundamental frequency data is the number of samples from the voice start time on the left and the fundamental frequency value on the right.

  FIG. 19 shows the configuration of the waveform generator 27. The waveform generation unit 27 generates a speech waveform from the prosodic data that is the output of the prosody generation unit 24. For this reason, the waveform generation unit 27 used in this embodiment includes a prosody data distribution unit 51 that distributes input prosodic data to phoneme string information, phoneme time length information, and pitch pattern information, and input phoneme string information. Selected from the phoneme sequence, the acoustic feature parameters corresponding to the selected phoneme sequence are sequentially read out from the speech data file 53 and output, and the acoustic feature parameters arranged on the time axis by the phoneme sequence And a parameter correction unit 54 that expands and contracts parameters on the time axis so as to be equal to the length of each phoneme by phoneme time information, a series of acoustic feature parameters output from the parameter correction unit 54, and prosody data Waveform assembly for synthesizing and outputting audio signal waveforms while changing the pitch based on the pitch pattern information from the distribution unit 51 Composed of 55.

  The voice data stored in the voice data file 53 included in the segment selection unit 52 is the same as that used in an existing regular voice synthesizer. For example, the voice data such as a cepstrum coefficient is used. Parameters showing various features. Further, the length of each element may be stored without fixing the unit of synthesis in the unit corresponding to CV, CVC, or other synthesis unit, or in the corpus-based synthesis method.

  The general operation of the waveform generator 27 is as follows. The input prosodic data is divided into phoneme string information, phoneme time length information, and pitch pattern information by the prosody data distribution unit 51 and output. The phoneme string information is sent to the segment selection unit 52. The element selection unit 52 refers to the voice data file 53 based on the input phoneme string information, selects a phoneme string included in the phoneme information, and sequentially reads out acoustic feature parameters corresponding to the selected phoneme string. Output. The parameter correction unit 54 receives the acoustic feature parameters arranged on the time axis by the phoneme string, and expands / contracts the parameters on the time axis so as to be equal to the length of each phoneme by the phoneme time information. Further, the acoustic feature parameter is corrected so that the parameter changes smoothly in order to avoid inconsistency of the parameter at the connecting portion of the piece. Moreover, the strength of each phoneme is changed by changing the parameter. The waveform assembling unit 55 synthesizes the audio signal waveform while changing the pitch based on the series of acoustic feature parameters output from the parameter correcting unit 54 and based on the pitch pattern information from the prosody data distributing unit 51. Output.

  As described above, the speech synthesizer 1 according to the present embodiment has the user registration memo, the storage folder name, the information at the time of recording, and the reproduction frequency, which are information attached to the image or sound data from the image file of the Exif file or the audio file Alternatively, the preference information can be read out, a sentence can be generated based on the read out information, and the generated sentence can be synthesized as a voice and output as a voice. For this reason, when the speech synthesizer 1 of the present invention is applied to an image / audio recording / reproducing apparatus, an image recorded / reproduced by the image / audio recording / reproducing apparatus can be searched based on information guided by sound.

  According to the speech synthesizer of the present invention, the speech speed, volume, voice pitch, voice color, and prosodic features of the output voice can be changed. In the following, a method for changing the output speech in text speech synthesis will be described.

Each change is made by the following method.
Speaking speed = Duration length expansion / contraction volume = Parameter correction, Change voice pitch = Change fundamental frequency value Voice color = Change voice data Prosodic features = Change duration, parameter, fundamental frequency for each phoneme Prosody The characteristic features relate to the impression when listening to the voice, such as bright intonation, emphasized intonation, and dark intonation.

  Intense intonation can be achieved by synthesizing a little faster with a loud and loud sound. Emphasized intonation can be achieved by synthesizing the eyes a little slowly with a loud sound. In this way, different prosodic features can be realized by changing the pitch, length, and strength of the synthesized sound. As described above, according to the present invention, the speech speed, volume, voice pitch, voice color, and prosodic features of the output synthesized sound are changed according to information accompanying the image or the sound data.

  FIG. 20 is a configuration diagram of a speech synthesizer 60 that can change the synthesized sound to be output. The speech synthesizer 60 that can change the synthesized sound to be output has a speech synthesis controller 61 in addition to the speech synthesizer 1 having the configuration shown in FIG.

  The speech synthesis control unit 61 determines how to change the synthesized speech based on the information extracted by the information reading unit 2 and the sentence generated by the sentence generation, and the text speech synthesis unit 4 This is a module for controlling.

  The voice synthesis control unit 61 also has a sound effect database, and can output the sound effect in synchronization with the synthesized sound. For example, the speech synthesis control unit 61 holds a rule for performing speech synthesis with soft intonation when the sentence generated by the sentence generation unit 3 is generated based on a user registration memo. And

  The speech synthesis control unit 61 determines whether or not the sentence to be processed by the text speech synthesis unit 4 is generated from the user registration memo based on information obtained from the information reading unit 2 and the sentence generation unit 3. If the sentence is generated from the user registration memo, the speech synthesis control unit 61 changes the prosodic data, acoustic feature parameters, and speech data. For example, if the soft intonation is realized by a slow and gentle voice, the speech synthesis control unit 61 extends the value of the duration length of the prosodic data at the same rate. In addition, the voice data used by the waveform generation unit 27 of the text-to-speech synthesis unit 4 is replaced with voice data generated based on gentle voice data. Then, the value of the portion related to the volume of the acoustic feature parameter is reduced at the same rate. As a result, the output synthesized sound has soft intonation.

  Similarly, emphasis intonation can be realized depending on the reproduction frequency, and a smooth intonation can be realized depending on the preference information. Moreover, according to the time when the image or the sound data was recorded, the sound effect can be output in synchronization with the synthesized sound.

  The voice synthesizer 1 and the voice synthesizer 60 can be applied to a portable video / audio recording / reproducing apparatus. FIG. 21 is a diagram showing a configuration of a portable video / audio recording / reproducing apparatus incorporating the voice synthesizer 60.

  In FIG. 21, an image / audio recording / reproducing apparatus 70 performs a recording process and a reproduction process on a subject image captured by the camera imaging unit 72, an image compression process, and an image. An image recording / reproducing unit 73 that performs decompression processing, and an image display unit 75 that displays an image subjected to recording processing and reproduction processing by the image recording / reproducing unit 73 under the control of a system control unit 74 described later.

  The camera imaging unit 72 inputs the captured image to the image recording / reproducing unit 73. The image recording / reproducing unit 73 compresses the captured image captured by the camera imaging unit 72 and records the compressed image as a file in the captured image file database 76. The image recording / reproducing unit 73 reads and decompresses the file from the captured image file database 76. I do. Further, the image recording / reproducing unit 73 inputs an audio signal together with the image signal to the captured image file database 76 using a file format as necessary. The screen display unit 75 is preferably a flat panel display capable of displaying an image or an icon, and displays image data in the file and a thumbnail image which is a reduced image for heading corresponding to the image file. In addition, a photographed original image can be displayed. It also has a character input menu display function.

  In addition, the video / audio recording / reproducing apparatus 70 controls the information added to each original image subjected to the recording process by the image recording / reproducing unit 73 and the information described in the photographing media format by the system control unit 74. A captured image file database 76 stored as file data based on the above, an operation key unit 78 operated by a user, an operation input signal from the operation key unit 78, particularly image selection information data, registered character string information data A registration key control unit 77 that receives continuous reproduction start image designation data and the like and sends the data to the system control unit 74.

  The photographed image file database 76 is a writable non-volatile memory or a writable / erasable memory medium, and includes, for example, a removable medium such as a memory card, a card-type HDD, a removable optical disk, and the like in a removable medium format such as a DCF file system. Reading can be performed in compliance. Further, it is preferable that user registered character information can be input in conformity with the removable media format.

  The image / sound recording / reproducing apparatus 70 includes an image recording / reproducing unit 73, an image display unit 75, a captured image file database 76, and a system control unit 74 that controls each unit connected to the registration key control unit 77. The system control unit 74 includes a recording / playback control function unit 81, a camera state control function unit 82, a file system control function unit 83, and a user key setting function unit 84.

  The camera state function unit 82, the file system function unit 83, and the user key setting function unit 84 in the system control unit 74 constitute the information reading unit 2 of the speech synthesizer 60. That is, the information reading unit 2 receives information associated with the image or sound data from the photographed image file database via the camera state function unit 82, the file system function unit 83, and the user key setting function unit 84 in the system control unit 74. Is read. The information read by the information reading unit 2 is supplied to the sentence generation unit 3 that constitutes the speech synthesis unit 60. The sentence generation unit 3 creates a text based on the read information. The text-to-speech synthesizer 4 synthesizes the text created by the sentence generator 3 with speech and outputs it. The speech synthesis control unit 61 controls speech synthesis in the text speech synthesis unit 4.

  The camera information control function unit 82 constituting the information reading unit 2 supplies information related to the state of the digital camera to the sentence generation unit 3. The state of the digital camera refers to the language mode in which the application being used is activated, the date, time, and the call menu mode at that time.

  The file system control unit 83 supplies information related to the image to the sentence generation unit 3. The information related to the image refers to the date and time of the image, the file extension, the frequency of access to the image, the name of the folder in which the image is stored, user comments, sensitivity key information, and the like.

  The user key setting control function unit 84 supplies information designated by the user for the output voice. The information specified by the user for the output voice refers to the language, volume, speed, voice pitch, etc. of the output voice.

  As described above, the speech synthesizer 60 is a user registration memo, storage folder name, information at the time of recording, reproduction frequency, or preference information that is an image file of an Exif file or information associated with an image or sound data from an audio file. Sensitivity information can be read, a sentence can be generated based on the read information, and the generated sentence can be synthesized as a voice and output as a voice. Moreover, since the speech synthesis unit 60 includes the speech synthesis control unit 61, when the sentence is generated from the user registration memo, the prosody data, the acoustic feature parameter, and the speech data are changed. Thereby, a soft intonation can be given to a synthetic sound. Similarly, emphasis intonation can be realized depending on the reproduction frequency, and a smooth intonation can be realized depending on the preference information. Moreover, according to the time when the image or the sound data was recorded, the sound effect can be output in synchronization with the synthesized sound.

  By providing such a voice synthesizing unit 60, the audio / video recording / reproducing apparatus 70 can make the user easily search for recorded / reproduced images based on information guided by voice.

  Note that the speech synthesizer according to the present invention may be used as an image search device that searches for images recorded and recorded by a digital camera. The image search apparatus includes a sentence generation unit that generates text based on information obtained from a digital camera, and a text-to-speech synthesis unit that synthesizes speech based on the text generated by the sentence generation unit. Read a text sentence generated by the generation means.

  In this image search device, the sentence generation unit generates text based on information described in the shooting media format of the digital camera, that is, information attached to the image or sound data. The sentence generation unit generates a text based on the user registration memo. The sentence generation unit generates a text based on the name of the folder storing the image data. The sentence generation means changes the number of sentences to be generated based on information obtained from the digital camera. The sentence generation means may change the number of sentences to be generated based on the access frequency to the image. Further, the number of sentences generated based on the likability key may be changed.

  The sentence generation means extracts a word from the user registration memo and applies it to a template to generate a sentence. At this time, the sentence generation unit generates a sentence by sequentially applying the character strings described in the user registration memo to blanks of the template. The sentence generation unit analyzes the character string described in the user registration memo, extracts a word, and applies it to a blank field of the template to generate a sentence. Further, the sentence generation means transforms the character string described in the user registration memo so as to be applied to the template, and applies it to the blank of the template to generate the sentence. The sentence generation unit analyzes the character string described in the user registration memo, extracts a word, transforms it so as to apply to the template, and generates a sentence by applying it to the blank of the template.

  Further, the sentence generation means selects another word from the concept dictionary using the words described in the user registration memo, and generates a sentence by applying it to the template. The sentence generation means selects words having the same semantic information from the concept dictionary using the words described in the user registration memo, and generates the sentence by applying it to the template. The sentence generation means selects a word of a higher concept of the word from the concept dictionary using the words described in the user registration memo, and generates the sentence by applying it to the template.

  Further, the sentence generation means may generate a sentence by applying a folder name for storing image data to a template. Further, the sentence generation means may extract time information from the shooting information data and apply it to a template to generate a sentence. The sentence generation means selects a template to be used according to the type of information obtained from the digital camera. The sentence generation unit may use the access frequency for each user as information obtained from the digital camera. Further, the sentence generation means may use likability information based on the likability key as information obtained from the digital camera. The sentence generation unit may select a template to be used as information obtained from the digital camera according to the extension of the file.

  In addition, the sentence generation unit may select a template to be used by using information accompanying other image files to be searched. A template to be used is selected using the time when the image was taken as information accompanying other image files to be searched. The sentence generation unit may select a template to be used by using a word described in a user registration memo as information attached to another image file to be searched. Further, the sentence generation unit extracts a word from the user registration memo, and selects a template using any one or more of word notation, part of speech, and semantic information as information of the word described in the memo.

  In the image search device, the text-to-speech synthesizer changes the language of the synthesized speech to be output according to language information obtained from the digital camera. The text-to-speech synthesizer may output a language specified by the user as the language of the synthesized sound. The text-to-speech synthesizer may select a language to be used from the mode of the digital camera. The text-to-speech synthesizer analyzes a character string included in the user registration memo and performs language determination. The text-to-speech synthesizer has a function of converting the generated sentence into a sentence of the determined language when the determined language is different from the language of the sentence generated by the sentence generator.

  The text-to-speech synthesizer changes the style used when outputting the sentence generated by the sentence generator according to the content of information obtained from the digital camera. The text-to-speech synthesizer includes, as information obtained from the digital camera, the contents of the user registration memo, shooting information data, shooting time, shooting location, frequency of access to the image, preference information by the preference key, and call mode information. By using any one or more of the menus, the style used when outputting the sentence generated by the sentence generation means is changed. The text-to-speech synthesizer changes the style used when the sentence generated by the sentence generator is output depending on the season, time zone, and date when the apparatus is used.

  The text-to-speech synthesis unit switches the prosody of the synthesized speech when outputting the sentence generated by the sentence generation unit according to the content of information obtained from the digital camera. The text-to-speech synthesizer switches one or more of the fundamental frequency and the phoneme duration time as the prosody.

  The text-to-speech synthesizer includes, as information obtained from the digital camera, the contents of the user registration memo, shooting information data, shooting time, shooting location, frequency of access to the image, preference information by the preference key, and call mode information. By using any one or more of the menus, the prosody of the synthesized speech when outputting the sentence generated by the sentence generating means is switched. The text-to-speech synthesis unit changes the prosody of the synthesized speech when the sentence generated by the sentence generation unit is output according to the season, time zone, and day in which the apparatus is used.

  The text-to-speech synthesis unit determines the speech speed of the synthesized speech when outputting the sentence generated by the sentence generation unit according to the content of information obtained from the digital camera. In addition, the text-to-speech synthesizer may be any of the information obtained from the digital camera, such as user registration memo contents, shooting information data, shooting time, shooting location, frequency of access to images, preference key, and call mode menu. By using one or more, the speech speed of the synthesized speech when outputting the sentence generated by the sentence generation means is determined. The text-to-speech synthesis unit determines the speech speed of the synthesized speech when outputting the sentence generated by the sentence generation unit according to the season, time zone, and day in which the apparatus is used.

  The text-to-speech synthesizer determines the volume level of the synthesized speech when outputting the sentence generated by the sentence generator according to the content of information obtained from the digital camera. In addition, the text-to-speech synthesizer may be any of the information obtained from the digital camera, such as user registration memo contents, shooting information data, shooting time, shooting location, frequency of access to images, preference key, and call mode menu. By using one or more, the volume level of the synthesized speech when the sentence generated by the sentence generating means is output is determined. The text-to-speech synthesis unit determines the volume level of the synthesized speech when outputting the sentence generated by the sentence generation unit according to the season, time zone, and day in which the apparatus is used.

  The text-to-speech synthesis unit determines a voice color of the synthesized speech when outputting the sentence generated by the sentence generation unit according to the content of information obtained from the digital camera. The text-to-speech synthesizer is one of a user registration memo content, shooting information data, shooting time, shooting location, frequency of access to images, preference key, and call mode menu as information obtained from the digital camera. By using two or more, the voice color of the synthesized speech when the sentence generated by the sentence generator is output is determined. The text-to-speech synthesis unit determines a voice color of the synthesized speech when outputting the sentence generated by the sentence generation unit according to the season, time zone, and day in which the apparatus is used.

  The text-to-speech synthesizer determines whether or not to add sound effects to the synthesized speech when outputting the sentence generated by the sentence generator according to the content of information obtained from the digital camera. In addition, the text-to-speech synthesizer may be any of the information obtained from the digital camera, such as user registration memo contents, shooting information data, shooting time, shooting location, frequency of access to images, preference key, and call mode menu. By using one or more, it is determined whether or not to add sound effects to the synthesized speech when outputting the sentence generated by the sentence generation means. The text-to-speech synthesizer determines whether to add sound effects to the synthesized speech when the sentence generated by the sentence generator is output, depending on the season, time zone, and day in which the apparatus is used. . The text-to-speech synthesizer determines the type of sound effect to be added to the synthesized speech when outputting the sentence generated by the sentence generator according to the content of information obtained from the digital camera. The text-to-speech synthesizer determines the type of sound effect to be added to the synthesized speech when the sentence generated by the sentence generator is output depending on the season, time zone, and day in which the apparatus is used.

It is a block diagram of a speech synthesizer. It is a flowchart which shows the whole process sequence of a speech synthesizer. It is a figure which shows the example of an image file structure of an Exif file. It is a figure which shows the audio file structural example of an Exif file. It is a figure which shows the corresponding | compatible table for the information reading which the information reading part hold | maintains. It is a figure which shows the example of the data read by the information reading part. It is a block diagram which shows the structure of a sentence production | generation part. It is a block diagram which shows the structure of an output content determination part. It is a flowchart which shows the process sequence of an output content determination part. It is a figure which shows the example of the data analyzed and output by a character string analysis part. It is a figure which shows the specific example of a template. It is a block diagram which shows the structure of the sentence production | generation part further in a sentence production | generation part. It is a figure which shows the specific example of the rule for structure determination which a sentence structure determination part has. It is a block diagram which shows the structure of a text-to-speech synthesis part. It is a block diagram which shows the structure of a language processing part. It is a block diagram which shows the structure of a prosody generation part. It is a figure which shows the duration time data of the first half part of prosodic data. It is a figure which shows the fundamental frequency data of the latter half part of prosody data. It is a block diagram which shows the structure of a waveform generation part. It is a block diagram which shows the structure of the speech synthesizer provided with the speech synthesis control part. It is a block diagram which shows the structure of the image audio | voice recording / reproducing apparatus provided with the speech synthesizer.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 Speech synthesizer, 2 Information reading part, 3 sentence production | generation part, 4 Text speech synthesizer, 5 Output content determination part, 7 Character string analysis part, 10 Output language determination part, 11 sentence content determination part, 12 sentence number determination Part, 13 template selection part, 14 template database

Claims (6)

Information reading means for reading information associated with image or sound data;
Sentence generation means for generating a sentence based on the information read by the information reading means;
Speech synthesis means for synthesizing and outputting the sentence generated by the sentence generation means,
The sentence generation means includes a character string analysis means for analyzing a character string based on information read by the information reading means in order to determine the content of a sentence to be output, and a character analyzed by the character string analysis means. A word extracting means for extracting a word from the sequence; and a template storage means for storing a template used for generating a sentence;
The sentence generation unit transforms the character string including the word extracted by the word extraction unit so as to apply to the rule for sentence structure determination requested by the template selected from the template storage unit, and the deformed character string. A speech synthesizer that generates a sentence by applying to the above template. A conceptual dictionary;
The sentence generation means selects a word corresponding to the character string indicating the concept from the words extracted by the word extraction means from the word dictionary indicating the concept in the template and applies it to the template. The speech synthesizer according to claim 1, which generates a sentence. Information accompanying the image or sound data includes a user registration memo, a storage folder name, or a reproduction frequency,
The speech synthesizer according to claim 2, wherein the speech synthesizer changes a prosody, speech speed, volume, or voice color of the synthesized speech in accordance with information accompanying the image or sound data.   4. The speech synthesizer according to claim 3, wherein the speech synthesizer changes the prosody to a soft intonation when speech synthesizing the user registration memo.   4. The speech synthesizer according to claim 3, wherein the speech synthesizer changes prosody, speech speed, volume, or voice color according to the time when the image or sound data is recorded. An information reading step for reading information associated with image or sound data;
A sentence generation step for generating a sentence based on the information read out by the information reading step;
A speech synthesis step of synthesizing and outputting the sentence generated by the sentence generation step,
The sentence generation step includes a character string analysis step for analyzing a character string based on the information read out by the information reading step in order to determine the content of the sentence to be output, and a character analyzed in the character string analysis step. A word extracting step of extracting a word from the column, and a template selecting step of selecting a template from a template storage means storing a template used for generating a sentence,
In the sentence generation step, the character string including the word extracted in the word extraction step is transformed so as to apply to the sentence structure determination rule required by the template selected from the template storage unit, and the transformed character string A speech synthesis method that generates a sentence by applying to the above template.

Images