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Speaker Recognition. Sharat.S.Chikkerur Center for Unified Biometrics and Sensors http://www.cubs.buffalo.edu. Speech Fundamentals. Characterizing speech Content (Speech recognition) Signal representation (Vocoding) Waveform Parametric( Excitation, Vocal Tract)
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Speaker Recognition Sharat.S.Chikkerur Center for Unified Biometrics and Sensors http://www.cubs.buffalo.edu
Speech Fundamentals • Characterizing speech • Content (Speech recognition) • Signal representation (Vocoding) • Waveform • Parametric( Excitation, Vocal Tract) • Signal analysis (Gender determination, Speaker recognition) • Terminologies • Phonemes : • Basic discrete units of speech. • English has around 42 phonemes. • Language specific • Types of speech • Voiced speech • Unvoiced speech(Fricatives) • Plosives • Formants
Pitch Av Impulse Train Generator Glottal Pulse Model G(z) Vocal Tract Model V(z) Radiation Model R(z) Noise source AN Speech production 17 cm Speech production mechanism Speech production model
Nature of speech Spectrogram
Vocal Tract modeling Smoothened Signal Spectrum Signal Spectrum • The smoothened spectrum indciates the locations of the formants of each user • The smoothened spectrum is obtained by cepstral coefficients
Parametric Representations: Formants • Formant Frequencies • Characterizes the frequency response of the vocal tract • Used in characterization of vowels • Can be used to determine the gender
Parametric Representations:LPC • Linear predictive coefficients • Used in vocoding • Spectral estimation 20 2 40 5 200
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)) Parametric Representations:Cepstrum 10 5 40
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.
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
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
Silence Removal • Preprocessing • Feature Extraction • Speaker model • Matching
Pre-emphasis • Preprocessing • Feature Extraction • Speaker model • Matching
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
Feature Representation • Preprocessing • Feature Extraction • Speaker model • Matching Speech signal and spectrum of two users uttering ‘ONE’
F1 = [a1…a10,b1…b10] F2 = [a1…a10,b1…b10] ……………. ……………. FN = [a1…a10,b1…b10] Speaker Model
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.
Results • Distances are normalized w.r.t. length of the speech signal • Intra speaker distance less than inter speaker distance • Distance matrix is symmetric
