Investigation on Inter-Speaker Variability in The Feature Space

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Investigation on Inter-Speaker Variability in The
Feature Space

• Presenter ???

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Reference

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

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Outline

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

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Introduction

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

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Introduction(2)

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

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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

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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

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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
• total mean vector
• 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
• of
• The radius of the scattering volume
• Higher
• 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

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Vocal tract normalization(3)

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

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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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Cepstral mean and variance normalization(2)

• 42 male 42 female 42 male