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A STUDY ON SPEECH RECOGNITION USING DYNAMIC TIME WARPING CS 525 : Project Presentation PALDEN LAMA and MOUNIKA NAMBURU.

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Presentation on theme: "A STUDY ON SPEECH RECOGNITION USING DYNAMIC TIME WARPING CS 525 : Project Presentation PALDEN LAMA and MOUNIKA NAMBURU."— Presentation transcript:

1 A STUDY ON SPEECH RECOGNITION USING DYNAMIC TIME WARPING CS 525 : Project Presentation PALDEN LAMA and MOUNIKA NAMBURU

2 G OALS Learn how it works ! Focus: Pre-Processing Dynamic Time Warping/Dynamic Programming Verify using MATLAB Build a simple Voice to Text Converter application.

3 H OW DOES IT WORK ? Record Extract a voice Feature Vectors Digitized Speech Signal (.wave file) Acoustic Preprocessing (DFT + MFCC) Speech Recognizer (Dynamic Time Warping)

4

5 S PEECH SIGNAL Voiced Excitation  fundamental frequency (Speaker dependent) Loudness  signal amplitude Vocal tract shape  spectral shaping (most important to recognize words) A time signal of vowel /a:/ (fs=11 kHz, length=100ms) time

6 ACOUSTIC PRE-PROCESSING Log power spectrum of a vowel /a:/ Cepstrum of a vowel /a:/ Power spectrum of the vowel /a:/ after cepstral smoothing (liftering) LIFTERING IDFTDFT

7 MFCC (M EL FREQUENCY CEPSTRAL COEFFICIENTS ) The difference between the cepstrum and the mel-frequency cepstrum is that in the MFC, the frequency bands are equally spaced on the mel scale, which approximates the human auditory system's response more closely than the linearly-spaced frequency bands used in the normal cepstrum.cepstrummel scale Coeff. Of power spectrum  Mel Spectral Coeff. (FEATURE VECTOR)

8 RECOGNIZER One word spoken contains dozens of feature vectors. (preprocessing every 10 ms of signal) Compute a ”distance” between this unknown sequence of vectors (unknown word) and known sequence of vectors (prototypes of words to recognize) PROBLEM !! Unequal length of vector sequence

9 D YNAMIC TIME WARPING : F IND OPTIMAL ASSIGNMENT PATH

10

11

12 DTW : R ECOGNIZING CONNECTED WORDS

13 MATLAB FUNCTIONS PRE-PROCESSING recordMelMatrix(3) S = wavread(“speech.wav”) C = Melfiltermatrix(S, N, K) computeMelSpectrum( C,S); DISPLAY FEATURES Featuredisp.m WORD RECOGNITION dp_asym(vector1, vector2)

14 R ESULTS hellohello1

15 libraryhello

16 computerhello

17 3.0304e+003 3.5820e+003 3.4499e+00 3

18 Welcome home (male) Welcome home (female)

19 Welcome homeWelcome back

20 Welcome homeComputer Science

21 Welcome backComputer Science

22 2.6418e+003 2.9468e+003 3.8109e+003 4.6701e+003

23 THANKS ! ANY QUESTIONS?

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