Man-Wai, 2016 - Google Patents
Feature selection and nuisance attribute projection for speech emotion recognitionMan-Wai, 2016
View PDF- Document ID
- 11264041377745177056
- Author
- Man-Wai M
- Publication year
External Links
Snippet
In speech emotion recognition, we need to maximize the variability of emotion features across emotion states and suppress the non-emotion variabilities. This paper investigates methods to achieve both of these goals. Specifically, utterance-level emotion features are …
- 230000001537 neural 0 abstract description 5
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
- G06K9/6807—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries
- G06K9/6842—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries according to the linguistic properties, e.g. English, German
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
- G10L15/065—Adaptation
- G10L15/07—Adaptation to the speaker
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/30707—Clustering or classification into predefined classes
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L17/00—Speaker identification or verification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2765—Recognition
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination
- G10L25/66—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Cao et al. | Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech | |
Lozano-Diez et al. | An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition | |
Gharavian et al. | Speech emotion recognition using FCBF feature selection method and GA-optimized fuzzy ARTMAP neural network | |
Altun et al. | Boosting selection of speech related features to improve performance of multi-class SVMs in emotion detection | |
Semwal et al. | Automatic speech emotion detection system using multi-domain acoustic feature selection and classification models | |
Sheikh et al. | Introducing ECAPA-TDNN and Wav2Vec2. 0 embeddings to stuttering detection | |
Yang et al. | Enhanced multiclass SVM with thresholding fusion for speech-based emotion classification | |
Sarkar et al. | Time-contrastive learning based deep bottleneck features for text-dependent speaker verification | |
Shah et al. | Within and cross-corpus speech emotion recognition using latent topic model-based features | |
Joshi et al. | A Study of speech emotion recognition methods | |
Bone et al. | Intoxicated speech detection: A fusion framework with speaker-normalized hierarchical functionals and GMM supervectors | |
Ma et al. | Speech Emotion Recognition with Emotion-Pair Based Framework Considering Emotion Distribution Information in Dimensional Emotion Space. | |
Singh et al. | Survey on human emotion recognition: Speech database, features and classification | |
Mishra et al. | Gender differentiated convolutional neural networks for speech emotion recognition | |
Monteiro et al. | An end-to-end approach for the verification problem: learning the right distance | |
Savchenko et al. | Speaker-aware training of speech emotion classifier with speaker recognition | |
Ma et al. | Spoken language recognition using ensemble classifiers | |
Zhou et al. | Speech Emotion Recognition with Discriminative Feature Learning. | |
Man-Wai | Feature selection and nuisance attribute projection for speech emotion recognition | |
Sheikh et al. | Stuttering detection using speaker representations and self-supervised contextual embeddings | |
Jiang et al. | Comparing feature dimension reduction algorithms for GMM-SVM based speech emotion recognition | |
Egas-López et al. | Predicting a cold from speech using fisher vectors; svm and xgboost as classifiers | |
Gosztolya et al. | A feature selection-based speaker clustering method for paralinguistic tasks | |
Charoendee et al. | Speech emotion recognition using derived features from speech segment and kernel principal component analysis | |
Kumar et al. | Speech And Emotion Recognition Using Deep Learning |