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Speaker Recognition

Speaker Recognition. Sharat.S.Chikkerur S.Anand Mantravadi Rajeev.K.Srinivasan. Speaker Recognition. Speaker Identification. Speaker Detection. Speaker Verification. Text Dependent. Text Independent. Text Dependent. Text Independent. Speaker Recognition. Definition

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Speaker Recognition

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  1. Speaker Recognition Sharat.S.Chikkerur S.Anand Mantravadi Rajeev.K.Srinivasan

  2. Speaker Recognition Speaker Identification Speaker Detection Speaker Verification Text Dependent Text Independent Text Dependent Text Independent Speaker Recognition • Definition • It is the method of recognizing a person based on his voice • It is one of the forms of biometric identification • Depends of speaker dependent characteristics. EE 516 Term Project, Fall 2003

  3. Pitch Av Impulse Train Generator Glottal Pulse Model G(z) Vocal Tract Model V(z) Radiation Model R(z) Noise source AN Speech production Speech production mechanism Speech production model EE 516 Term Project, Fall 2003

  4. Generic Speaker Recognition System Speech signal Score Analysis Frames Feature Vector Preprocessing Feature Extraction Pattern Matching Verification Preprocessing Feature Extraction Speaker Model Enrollment • Stochastic Models • GMM • HMM • Template Models • DTW • Distance Measures • LAR • Cepstrum • LPCC • MFCC • A/D Conversion • End point detection • Pre-emphasis filter • Segmentation • Choice of features • Differentiating factors b/w speakers include vocal tract shape and behavioral traits • Features should have high inter-speaker and low intra speaker variation EE 516 Term Project, Fall 2003

  5. Our Approach Silence Removal Cepstrum Coefficients Cepstral Normalization Long time average Polynomial Function Expansion Reference Template Dynamic Time Warping Distance Computation • Preprocessing • Feature Extraction • Speaker model • Matching EE 516 Term Project, Fall 2003

  6. Silence Removal • Preprocessing • Feature Extraction • Speaker model • Matching EE 516 Term Project, Fall 2003

  7. Pre-emphasis • Preprocessing • Feature Extraction • Speaker model • Matching EE 516 Term Project, Fall 2003

  8. Segmentation • Preprocessing • Feature Extraction • Speaker model • Matching • Short time analysis • The speech signal is segmented into overlapping ‘Analysis Frames’ • The speech signal is assumed to be stationary within this frame Q31 Q32 Q33 Q34 EE 516 Term Project, Fall 2003

  9. Feature Representation • Preprocessing • Feature Extraction • Speaker model • Matching Speech signal and spectrum of two users uttering ‘ONE’ EE 516 Term Project, Fall 2003

  10. Vocal Tract modeling • Preprocessing • Feature Extraction • Speaker model • Matching Signal Spectrum Smoothened Signal Spectrum • The smoothened spectrum indciates the locations of the formants of each user • The smoothened spectrum is obtained by cepstral coefficients EE 516 Term Project, Fall 2003

  11. Pitch Av P[n] G(z) V(z) R(z) y1‘[n]+y2‘[n] x1‘[n]+x2‘[n] x1[n]*x2[n] y1[n]*y2[n] D[] L[] D-1[] u[n] x1[n]*x2[n] x1‘[n]+x2‘[n] DFT[] LOG[] IDFT[] AN X1(z)X2(z) log(X1(z)) + log(X2(z)) Cepstral coefficients • Preprocessing • Feature Extraction • Speaker model • Matching EE 516 Term Project, Fall 2003

  12. F1 = [a1…a10,b1…b10] F2 = [a1…a10,b1…b10] ……………. ……………. FN = [a1…a10,b1…b10] Speaker Model EE 516 Term Project, Fall 2003

  13. Dynamic Time Warping • Preprocessing • Feature Extraction • Speaker model • Matching • The DTW warping path in the n-by-m matrix is the path which has minimum average cumulative cost. The unmarked area is the constrain that path is allowed to go. EE 516 Term Project, Fall 2003

  14. Results • Distances are normalized w.r.t. length of the speech signal • Intra speaker distance less than inter speaker distance • Distance matrix is symmetric EE 516 Term Project, Fall 2003

  15. Matlab Implementation EE 516 Term Project, Fall 2003

  16. THANK YOU

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