Chapter 9. Linear Predictive Analysis of Speech Signals 语音信号的线性预测分析

Transcription

1 Chapter 9 Linear Predictive Analysis of Speech Signals 语音信号的线性预测分析 1

2 LPC Methods LPC methods are the most widely used in speech coding, speech synthesis, speech recognition, speaker recognition and verification and for speech storage LPC methods provide extremely accurate estimates of speech parameters, and does it extremely efficiently basic idea of Linear Prediction: current speech sample can be closely approximated as a linear combination of past samples, i.e., 2

3 LPC Methods for periodic signals with N p period, it is obvious that but that is not what LP is doing; it is estimating s(n) from the p (p<< N p ) most recent values of s(n) by linearly predicting its value for LP, the predictor coefficients (the α k 's) are determined (computed) by minimizing the sum of squared differences (over a finite interval) between the actual speech samples and the linearly predicted ones 3

4 Speech Production Model the time-varying digital filter represents the effects of the glottal pulse shape, the vocal tract IR, and radiation at the lips the system is excited by an impulse train for voiced speech, or a random noise sequence for unvoiced speech this all-pole model is a natural representation for non-nasal voiced speech but it also works reasonably well for nasals and unvoiced sounds 4

5 Linear Prediction Model a p-th order linear predictor is a system of the form the prediction error, e(n), is of the form the prediction error is the output of a system with transfer function 5

6 LP Estimation Issues need to determine {α k } directly from speech such that they give good estimates of the time-varying spectrum need to estimate {α k } from short segments of speech minimize mean-squared prediction error over short segments of speech if the speech signal obeys the production model exactly, then α k =a k e(n) = Gu(n) A(z) is an inverse filter for H(z) 6

7 Solution for {α k } short-time average prediction squared-error is defined as select segment of speech in the vicinity of sample the key issue to resolve is the range of m for summation (to be discussed later) 7

8 Solution for {α k } can find values of α k that minimize by setting giving the set of equations where are the values of α k that minimize (from now on just use α k rather than for the optimum values) prediction error is orthogonal to signal for delays (i) of 1 to p 8

9 Solution for {α k } defining we get leading to a set of p equations in p unknowns that can be solved in an efficient manner for the {α k } 9

10 Solution for {α k } minimum mean-squared prediction error has the form which can be written in the form Process Compute for Solve matrix equation for α k need to specify range of m to compute need to specify 10

11 Autocorrelation Method assume exists for and is exactly zero everywhere else (i.e., window of length L samples) (Assumption #1) where w(m) is a finite length window of length L samples 11

12 Autocorrelation Method if is non-zero only for, then is non-zero only over the interval, giving at values of m near 0 (i.e. m = 0,1,,p-1) we are predicting signal from zero-valued samples outside the window range => will be (relatively) large at values near m=l (i.e. m = L,L+1,,L+p-1) we are predicting zero-valued samples (outside window range) from non-zero values => will be (relatively) large for these reasons, normally use windows that taper the segment to zero (e.g., Hamming window) 12

13 Autocorrelation Method 13

14 Autocorrelation Method for calculation of since outside the range then which is equivalent to the form can easily show that where is the shot-time autocorrelation of evaluated at i-k, where 14

15 Autocorrelation Method since is even, then thus the basic equation becomes with the minimum mean-squared prediction error of the form 15

16 Autocorrelation Method as expressed in matrix form with solution is a pxp Toeplitz Matrix => symmetric with all diagonal elements equal => there exist more efficient algorithms to solve for {α k } than simple matrix inversion 16

17 Covariance Method there is a second basic approach to defining the speech segment and the limits on the sums, namely fix the interval over which the mean-squared error is computed, giving (Assumption #2) 17

18 Covariance Method changing the summation index gives key difference from Autocorrelation Method is that limits of summation include terms before m = 0 => window extends p samples backwards from to since we are extending window backwards, don't need to taper it using a HW- since there is no transition at window edges 18

19 Covariance Method 19

20 Covariance Method cannot use autocorrelation formulation => this is a true cross correlation need to solve set of equations of the form 20

21 Covariance Method we have => symmetric but not Toeplitz matrix all terms have a fixed number of terms contributing to the computed values (L terms) is a covariance matrix => specialized solution for {α k } called the Covariance Method 21

22 LPC Summary 1. Speech Production Model 2. Linear Prediction Model 22

23 LPC Summary 3. LPC Minimization 23

24 LPC Summary 4. Autocorrelation Method 24

25 LPC Summary 4. Autocorrelation Method resulting matrix equation matrix equation solved using Levinsn-Durbin method 25

26 5. Covariance Method fix interval for error signal LPC Summary need signal for from to => L+p samples expressed as a matrix equation 26

27 Frequency Domain Interpretations of Linear Predictive Analysis 27

28 The Resulting LPC Model The final LPC model consists of the LPC parameters, {α k }, k=1,2,,p, and the gain, G, which together define the system function with frequency response with the gain determined by matching the energy of the model to the short-time energy of the speech signal, i.e., 28

29 LPC Spectrum LP Analysis is seen to be a method of short-time spectrum estimation with removal of excitation fine structure (a form of wideband spectrum analysis) 29

30 Effects of Model Order 30

31 Effects of Model Order plots show Fourier transform of segment and LP spectra for various orders as p increases, more details of the spectrum are preserved need to choose a value of p that represents the spectral effects of the glottal pulse, vocal tract and radiation-- nothing else 31

32 Linear Prediction Spectrogram Speech spectrogram previously defined as: for set of times,, and set of frequencies, where R is the time shift (in samples) between adjacent STFTS, T is the sampling period, F S = 1 / T is the sampling frequency, and N is the size of the discrete Fourier transform used to computed each STFT estimate. Similarly we can define the LP spectrogram as an image plot of: where and are the gain and prediction error polynomial at analysis time rr. 32

33 Linear Prediction Spectrogram Wideband Fourier spectrogram ( L=81, R=3, N=1000, 40 db dynamic range) Linear predictive spectrogram (p=12) 33

34 Comparison to Other Spectrum Analysis Methods Spectra of synthetic vowel /IY/ (a) Narrowband spectrum using 40 msec window (b) Wideband spectrum using a 10 msec window (c) Cepstrally smoothed spectrum (d) LPC spectrum from a 40 msec section using a p=12 order LPC analysis 34

35 Comparison to Other Spectrum Analysis Methods Natural speech spectral estimates using cepstral smoothing (solid line) and linear prediction analysis (dashed line). Note the fewer (spurious) peaks in the LP analysis spectrum since LP used p=12 which restricted the spectral match to a maximum of 6 resonance peaks. Note the narrow bandwidths of the LP resonances versus the cepstrally smoothed resonances. 35

36 Solutions of LPC Equations Autocorrelation Method (Levinson-Durbin Algorithm) 36

37 Levinson-Durbin Algorithm 1 Autocorrelation equations (at each frame ) R is a positive definite symmetric Toeplitz matrix The set of optimum predictor coefficients satisfy with minimum mean-squared prediction error of 37

38 Levinson-Durbin Algorithm 2 By combining the last two equations we get a larger matrix equation of the form: expanded (p+1)x(p+1) matrix is still Toeplitz and can be solved iteratively by incorporating new correlation value at each iteration and solving for higher order predictor in terms of new correlation value and previous predictor 38

39 Levinson-Durbin Algorithm 3 Show how i-th order solution can be derived from (i-1)-st order solution; i.e., given the solution to we derive solution to The (i-1)-st solution can be expressed as 39

40 Levinson-Durbin Algorithm 4 Appending a 0 to vector and multiplying by the matrix gives a new set of (i+1) equations of the form: where and R[i] are introduced 40

41 Levinson-Durbin Algorithm 5 Key step is that since Toeplitz matrix has special symmetry we can reverse the order of the equations (first equation last, last equation first), giving: 41

42 Levinson-Durbin Algorithm 6 To get the equation into the desired form (a single component in the vector ) we combine the two sets of matrices (with a multiplicative factor ) giving: Choose so that vector on right has only a single non-zero entry, i.e., 42

43 Levinson-Durbin Algorithm 7 The first element of the right hand side vector is now: The k i parameters are called PARCOR (partial correlation) coefficients With this choice of, the vector of i-th order predictor coefficients is: yielding the updating procedure 43

44 Levinson-Durbin Algorithm 8 The final solution for order p is: with prediction error If we use normalized autocorrelation coefficients: we get normalized errors of the form: where 44

45 Levinson-Durbin Algorithm 45

46 Autocorrelation Example consider a simple p = 2 solution of the form with solution 46

47 Autocorrelation Example with final coefficients 47

48 Prediction Error as a Function of p 48

49 Autocorrelation Method Properties mean-squared prediction error always non-zero decreases monotonically with increasing model order autocorrelation matching property model and data match up to order p spectrum matching property favors peaks of short-time FT minimum-phase property zeros of A(z) are inside the unit circle Levinson-Durbin recursion efficient algorithm for finding prediction coefficients PARCOR coefficients and MSE are by-products 49

50 The Prediction Error Signal 50

51 Prediction Error Signal Behavior 51

52 LP Speech Analysis file:s5, ss:11000, frame size (L):320, lpc order (p):14, cov method Top panel: speech signal Second panel: error signal Third panel: log magnitude spectra of signal and LP model Fourth panel: log magnitude spectrum of error signal 52

53 LP Speech Analysis file:s5, ss:11000, frame size (L):320, lpc order (p):14, ac method Top panel: speech signal Second panel: error signal Third panel: log magnitude spectra of signal and LP model Fourth panel: log magnitude spectrum of error signal 53

54 LP Speech Analysis file:s3, ss:14000, frame size (L):160, lpc order (p):16, cov method Top panel: speech signal Second panel: error signal Third panel: log magnitude spectra of signal and LP model Fourth panel: log magnitude spectrum of error signal 54

55 LP Speech Analysis file:s3, ss:14000, frame size (L):160, lpc order (p):16, ac method Top panel: speech signal Second panel: error signal Third panel: log magnitude spectra of signal and LP model Fourth panel: log magnitude spectrum of error signal 55

56 Properties of the LPC Polynomial 56

57 Minimum-Phase Property of A(z) Proof: Assume that is a zero (root) of A(z) The minimum mean-squared error is Thus, A(z) could not be the optimum filter because we could replace z 0 by and decrease the error 57

58 PARCORs and Stability Proof: It is easily shown that k i is the coefficient of z -i in A (i) (z), i.e., Therefore If k i 1, then either all the roots must be on the unit circle or at least one of them must be outside the unit circle k i <1 is a necessary and sufficient condition for A(z) to be a minimum phase system and 1/A(z) to be a stable system 58

59 Root Locations for Optimum LP Model 59

60 Pole-Zero Plot for Model 60

61 Pole Locations 61

62 Pole Locations (F S =10,000 Hz) 62

63 Estimating Formant Frequencies compute A(z) and factor it find roots that are close to the unit circle. compute equivalent analog frequencies from the angles of the roots. plot formant frequencies as a function of time. 63

64 Spectrogram with LPC Roots 64

65 Spectrogram with LPC Roots 65

66 Alternative Representations of the LP Parameters 66

67 LP Parameter Sets 67

68 PARCOR PARCORs to Prediction Coefficients assume that k i, i=1,2,, p are given. Then we can skip the computation of k i in the Levinson recursion. 68

69 PARCOR Prediction Coefficients to PARCORs assume that α j, j=1,2,, p are given. Then we can work backwards through the Levinson Recursion. 69

70 Log Area Ratio log area ratio coefficients from PARCOR coefficients with inverse relation 70

71 Roots of Predictor Polynomial roots of the predictor polynomial where each root can be expressed as a z-plane i.e., important for formant estimation 71

72 Impulse Response of H(z) IR of all pole system 72

73 LP Cepstrum cepstrum of IR of overall LP system from predictor coefficients predictor coefficients from cepstrum of IR where 73

74 autocorrelation of IR Autocorrelation of IR 74

75 Autocorrelation of Predictor Polynomial autocorrelation of the predictor polynomial with IR of the inverse filter with autocorrelation 75

76 Line Spectral Pairs Quantization of LP Parameters consider the magnitude-squared of the model frequency response where g is a parameter that affects P. spectral sensitivity can be defined as which measures sensitivity to errors in the g i parameters 76

77 Line Spectral Pairs spectral sensitivity for k i parameters; low sensitivity around 0; high sensitivity around 1 spectral sensitivity for log area ratio parameters, g i low sensitivity for virtually entire range is seen 77

78 Line Spectral Pairs Consider the following Form the symmetric polynomial P(z) as Form the anti-symmetric polynomial Q(z) as 78

79 LSP Example 79

80 Line Spectral Pairs properties of LSP parameters 1. all the roots of P(z) and Q(z) are on the unit circle 2. a necessary and sufficient condition for k i < 1, i = 1, 2,, p is that the roots of P(z) and Q(z) alternate on the unit circle 3. the LSP frequencies get close together when roots of A(z) are close to the unit circle 80

81 Applications 81

82 Speech Synthesis 82

83 Speech Coding 1. Extract α k parameters properly 2. Quantize α k parameters properly so that there is little quantization error Small number of bits go into coding the α k coefficients 3. Represent e(n) via: Pitch pulses and noise LPC Coding Multiple pulses per 10 msec interval MPLPC Coding Codebook vectors CELP Almost all of the coding bits go into coding of e(n) 83

84 LPC Vocoder 84