LPC for Speech Recognition

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LPC for Speech Recognition

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LPC for Speech Recognition

  1. 1.  By – Uday Saikia
  2. 2.  Speech compression/Speech coding is a method for reducing the amount of information needed to represent a speech signal.  LPC(Linear predictive coding)  One of the methods of compression that models the process of speech production.  A digital method for encoding an analog signal in which a particular value is predicted by a linear function of the past values of the signal.  At a particular time, t, the speech sample s(t) is represented as a linear sum of the p previous samples.  The general algorithm for linear predictive coding involves an analysis or encoding part and a synthesis or decoding part.
  3. 3.  In the encoding, LPC takes the speech signal in blocks or frames of speech and determines the input signal and the coefficients of the filter that will be capable of reproducing the current block of speech.  In the decoding, LPC rebuilds the filter based on the coefficients received.
  4. 4. X=wavread(‘Input Signal’); Frame=1:32; StartSample=(Frame-1)*250+1; EndSample=StartSample+249; Current=x(StartSample:EndSample); A=lpc(Current,10); C(Frame,1)=real(a(2)); For n=2:10 S=0; For m=1:n-1 S=s+ m*a(m+1)*c(Frame, n-m); End; C(Frame, n)=real(a(n+1)+s/n); End; For n=11:20 S=0; For m=1:10 S=s+(n-m)*a(m+1)*c(Frame, n-m); End; C(Frame, n)=real(s/n); End; X=real(c(20,1:20)); Plot(x, ‘b*-’);
  5. 5. VOWELS CEPSTRAL COEFFICIENT OF MALE CEPSTRAL COEFFICIENT OF FEMALE MAXIMUM MINIMUM RANGE OF VARIATION MAXIMUM MINIMUM /a/ 2.70 -1.42 4.12 12.29 -2.18 /e/ 1.70 -0.70 2.40 1.84 -1.88 /i/ 1.27 -0.74 2.01 1.84 -1.88 /o/ 1.98 -0.99 2.97 1.75 -1.33 /u/ 1.39 0.56 0.83 1.19 -1.19
  6. 6. Frame no.:11 10 Male Female 8 LogMagnitude(dB) 6 4 2 0 -2 -4 0 2 4 6 8 10 12 Cepstral Coefficients 14 16 18 20 Fig i(a): Cepstral characteristics of male and female for frame no.11
  7. 7. Frame no.:12 1.5 Male Female LogMagnitude(dB) 1 0.5 0 -0.5 -1 0 2 4 6 8 10 12 Cepstral Coefficients 14 16 18 Fig i(b): Cepstral characteristics of male and female for frame no.12 20
  8. 8. Frame no.:15 6 Male Female 5 LogMagnitude(dB) 4 3 2 1 0 -1 -2 -3 0 2 4 6 8 10 12 Cepstral Coefficients 14 16 18 20 Fig i( c): Cepstral characteristics of male and female for frame no.15
  9. 9. RESULTS AND DISCUSSION In the present study, it has been observed that by careful selection of the feature set (Frame no 11, Frame no 12, Frame no 15) could increase the efficiency of the recognizer. It provides a basis for Bodo vowel recognizer. Fig i(a), Fig i(b) , Fig i(c ) have shown the prominent distinction between male and female utterances for the Bodo vowel /a/.From our analysis for other vowels also, distinguishable feature vectors are found for male and female utterances for the same set of frames. It is thus concluded through the present study that LPCC measure may be a better technique for speaker identification with reference to sex of the speaker.

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