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Investigation on Inter-Speaker Variability in The Feature Space

Investigation on Inter-Speaker Variability in The Feature Space

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Investigation on Inter-Speaker Variability in The Feature Space

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  1. Investigation on Inter-Speaker Variability in The Feature Space Presenter : 陳彥達

  2. Reference • R. Haeb-Umbach, “Investigation on Inter-Speaker Variability in The Feature Space”, ICASSP 99.

  3. Outline • Introduction • A measure of inter-speaker variability • Vocal tract normalization • Cepstral mean and variance normalization

  4. Introduction • Adaptation • Reduce mismatch by adapting feature vectors or model parameters to the target environment.

  5. Introduction(2) • Normalization • Compute feature or model parameters that are insensitive to undesired variations of the speech signal.

  6. Introduction(3) • Fisher discriminant analysis • An early assessment of a feature set without running recognition first • The ratio of feature variability due to different phonemes and due to different speakers

  7. A measure of inter-speaker variability • Good feature vector space • Close together when belonging to the same phoneme class • Separated from each other when belonging to the different phoneme class

  8. A measure of inter-speaker variability(2) : cepstral feature vectors : cepstral mean feature vector : class mean vector : total mean vector

  9. A measure of inter-speaker variability(3) : cepstral mean feature vector : class mean vector : total mean vector : between class covariance matrix : within class covariance matrix

  10. A measure of inter-speaker variability(4) • Fisher variate analysis • = the sum of the eigenvalues of • The radius of the scattering volume • Higher lower recognition error rate

  11. Vocal tract normalization • Reduce inter-speaker variability by a speaker-specific frequency warping • Differences in vocal tract length are compensated for by a linear warping factor

  12. Vocal tract normalization(2) 42 male + 42 female 42 male

  13. Vocal tract normalization(3) a normalization on a per sentence basis performs better than a normalization on a per speaker basis

  14. Cepstral mean and variance normalization : input cepstral feature : estimate of the mean of the input cepstral feature : estimate of the standard deviation of the input cepstral feature : the mean and variance normalized feature : number of features

  15. Cepstral mean and variance normalization(2) 42 male + 42 female 42 male