Sarkar et al., 2014 - Google Patents
Stochastic feature compensation methods for speaker verification in noisy environmentsSarkar et al., 2014
- Document ID
- 2940818585815955872
- Author
- Sarkar S
- Rao K
- Publication year
- Publication venue
- Applied Soft Computing
External Links
Snippet
This paper explores the significance of stereo-based stochastic feature compensation (SFC) methods for robust speaker verification (SV) in mismatched training and test environments. Gaussian Mixture Model (GMM)-based SFC methods developed in past has been solely …
- 230000000996 additive 0 abstract description 26
Classifications
-
- 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
- G10L15/14—Speech classification or search using statistical models, e.g. hidden Markov models [HMMs]
- G10L15/142—Hidden Markov Models [HMMs]
- G10L15/144—Training of HMMs
-
- 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
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
- G10L21/0216—Noise filtering characterised by the method used for estimating noise
- G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
-
- 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
- G10L15/063—Training
- G10L2015/0635—Training updating or merging of old and new templates; Mean values; Weighting
- G10L2015/0636—Threshold criteria for the updating
-
- 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
- G10L17/04—Training, enrolment or model building
-
- 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
- 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
- G10L15/02—Feature extraction for speech recognition; Selection of recognition unit
-
- 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
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 characterised by the type of extracted parameters
- G10L25/09—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 characterised by the type of extracted parameters the extracted parameters being zero crossing rates
-
- 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
- G10L17/06—Decision making techniques; Pattern matching strategies
- G10L17/10—Multimodal systems, i.e. based on the integration of multiple recognition engines or fusion of expert systems
-
- 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
- G10L15/20—Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech
-
- 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
- G10L25/93—Discriminating between voiced and unvoiced parts of speech signals
-
- 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
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L19/00—Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signal, using source filter models or psychoacoustic analysis
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Lei et al. | A noise robust i-vector extractor using vector taylor series for speaker recognition | |
Cui et al. | Noise robust speech recognition using feature compensation based on polynomial regression of utterance SNR | |
Sholokhov et al. | Semi-supervised speech activity detection with an application to automatic speaker verification | |
Vu et al. | Combining non-negative matrix factorization and deep neural networks for speech enhancement and automatic speech recognition | |
Sarkar et al. | Stochastic feature compensation methods for speaker verification in noisy environments | |
Sehr et al. | Reverberation model-based decoding in the logmelspec domain for robust distant-talking speech recognition | |
CN103000174A (en) | Feature compensation method based on rapid noise estimation in speech recognition system | |
Mallidi et al. | Uncertainty estimation of DNN classifiers | |
Das et al. | Bangladeshi dialect recognition using Mel frequency cepstral coefficient, delta, delta-delta and Gaussian mixture model | |
Seo et al. | A maximum a posterior-based reconstruction approach to speech bandwidth expansion in noise | |
Li et al. | Improvements to VTS feature enhancement | |
Yao et al. | Noise adaptive speech recognition based on sequential noise parameter estimation | |
Milner et al. | Robust acoustic speech feature prediction from noisy mel-frequency cepstral coefficients | |
Asbai et al. | Improving the self-adaptive voice activity detector for speaker verification using map adaptation and asymmetric tapers | |
Dat et al. | On-line Gaussian mixture modeling in the log-power domain for signal-to-noise ratio estimation and speech enhancement | |
Ming et al. | An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion | |
Frankle et al. | Robust speaker identification under noisy conditions using feature compensation and signal to noise ratio estimation | |
Sim et al. | A trajectory-based parallel model combination with a unified static and dynamic parameter compensation for noisy speech recognition | |
Kim et al. | Variational noise model composition through model perturbation for robust speech recognition with time-varying background noise | |
Xiao et al. | Attribute-based histogram equalization (HEQ) and its adaptation for robust speech recognition. | |
Wu et al. | An environment compensated maximum likelihood training approach based on stochastic vector mapping [speech recognition applications] | |
Rao et al. | Stochastic feature compensation for robust speaker verification | |
Tsao et al. | An ensemble modeling approach to joint characterization of speaker and speaking environments. | |
Astudillo et al. | Propagation of Statistical Information Through Non‐Linear Feature Extractions for Robust Speech Recognition | |
Du et al. | IVN-based joint training of GMM and HMMs using an improved VTS-based feature compensation for noisy speech recognition |