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Robust Speech Feature

Robust Speech Feature. Decorrelated and Liftered Filter-Bank Energies (DLFBE) Proposed by K.K. Paliwal , in EuroSpeech 99. DLFBE ---Preliminary. * MFCC is very successful in speech recognition * MFCC computed from the speech signal using

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Robust Speech Feature

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  1. Robust Speech Feature Decorrelated and Liftered Filter-Bank Energies (DLFBE) Proposed by K.K. Paliwal , in EuroSpeech 99

  2. DLFBE ---Preliminary * MFCC is very successful in speech recognition * MFCC computed from the speech signal using the following three steps: 1.Compute the FFT power spectrum of the speech signal 2.Apply a Mel-space filter-bank to the power spectrum to get N energies (N=20~60) 3.Compute discrete cosine x’form (DCT) of log filter-bank energies to get uncorrelated MFCC’s (M=10)

  3. DLFBE --- Motivation *MFCC has two drawbacks 1. Does not have any physical interpretataion 2. Liftering of cepstral coefficient has no effect in the modern speech recognition (discuss later) *The two problem(i.e., numbers and correlation) in FBE used in 50’s, 60’s,70’s can be solved today

  4. Liftering --- What and How Euclidean distance *Lifter is the reweighting process of cepstral coeff. used in DTW framework of speech recognition where is dissimilarity between the test vector and the mean vector

  5. Liftering --- What and How (cont’d) Where is i-th cepstral coeff. , is the corresponding liftering coeff. and is the lifter So More general form

  6. Liftering --- What and How (cont’d)

  7. Liftering --- What and How (cont’d) The types of lifters are listed belows 1.Linear lifter 2.Statistical lifter 3.Sinusoidal lifter 4.Exponential lifter

  8. Liftering --- Discussion and Why * The multiplicative weighting in cepstrum domain is equivalent to convolution in spectral domain

  9. Liftering --- Experiment on DTW

  10. Liftering on CDHMM (??) --- Why Mahalanobis distance measure due to out observation prob.

  11. Liftering on CDHMM (??) --- Why liftering matrix for MFCC where

  12. Liftering on CDHMM (??) --- Why Thus,cepstral liftering has no effect in the recognition process when used with continuous observation Gaussian Density HMM’s

  13. Decorrelation of FBE --- Why/How *FBEs are correlated => we can’t use CDHMM * We can use LP techniques to solve this defeat can be obtained by covariance method of LP analysis

  14. Liftering of FBE --- How N=M+L FIR filter

  15. DLFBE --- Experiment *SI and isolated word recognition using ISOLET spoken letter database *90 training utterances from 90 speakers(45 females,45 males) 30 testing utterances from 30 speakers (15 females,15 males)

  16. DLFBE --- Experiment (cont’d)

  17. DLFBE --- Experiment (cont’d)

  18. Robust Speech Feature Noise-Invariant Representation for Speech Signal Group Delay Function (GDF) Method Proposed by Bayya & Yegnanarayana in EuroSpeech ‘99

  19. GDF --- Motivation *Background noise is a prominent source of mismatch and eliminated roughly by methods as follows 1.compensation cause the overestimation and underestimation side effects

  20. GDF --- Motivation (cont’d) 2.new feature not completely noise resistant *All the above use power/amplitude as speech feature Why don’t we use phase information as features ? And phase infor. may be helpful in speech recognition.

  21. GDF --- What/How *GDF is defined as the normalized autocorrelation of a short segment of a signal (#.1) Where is the normalized autocorrelation of a short segment of a signal

  22. GDF --- What/How (cont’d) (#.2) compare(#.1)&(#.2)

  23. GDF --- What/How (cont’d) Easy to implement Truncated version of GDF

  24. GDF --- What/How (cont’d) where Hanning window

  25. GDF --- Why & Experiment *frame length = 5 ms , frame rate = 1 ms & modified autocorrelation sequence averaged over 20 frames then the GDF computed as defined above

  26. GDF --- Why & Experiment (cont’d)

  27. GDF --- Experiment *Isolated-digit recognition ? Due to large dynamic range

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