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Enhancing Speech Recognition: The DLFBE Method and GDF Approach

This paper explores the robust speech feature extraction techniques proposed by K.K. Paliwal in EuroSpeech '99. It discusses the Discriminative Liftered Filter-Bank Energies (DLFBE) and their potential advantages over traditional MFCC in speech recognition, focusing on issues such as physical interpretation and correlation. The paper also presents the Group Delay Function (GDF) method, which utilizes phase information for noise-invariant speech recognition, highlighting its implementation and effectiveness in experiments with isolated-word recognition.

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Enhancing Speech Recognition: The DLFBE Method and GDF Approach

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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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