Investigation on Inter-Speaker Variability in The Feature Space

Presenter : 陳彥達

Reference

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

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

Introduction Adaptation

Reduce mismatch by adapting feature vectors or model parameters to the target environment.

Introduction(2) Normalization

Compute feature or model parameters that are insensitive to undesired variations of the speech signal.

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

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

A measure of inter-speaker variability(2)

: cepstral feature vectors

: cepstral mean feature vector

: class mean vector

: total mean vector

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A measure of inter-speaker variability(3)

: cepstral mean feature vector : class mean vector

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: between class covariance matrix

: within class covariance matrix

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A measure of inter-speaker variability(4)

Fisher variate analysis = the sum of the eigenvalues

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lower recognition error rate

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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

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Vocal tract normalization(2)

42 male + 42 female 42 male

Vocal tract normalization(3)

a normalization on a per sentence basis performs better than a normalization on a per speaker basis

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

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42 male + 42 female 42 male