Mfcc & czt

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Mfcc & czt

  1. 1. SEMINAR ONAcoustic Feature Comparison of MFCC andCZT-based Cepstrum for Speech Recognition Guided by:- Presented by:- Prof. R.V.Pawar Neehal B. Jiwane
  2. 2. Introduction The Mel-Frequency Cepstral Coefficients (MFCC) are the mostwidely used features in speech recognition field. Automatic speech recognition (ASR) systems. Feature extraction. The MFCC parameters perform better than others in the recognitionaccuracy.
  3. 3. MFCCFig. 1. MFCC Block Diagram
  4. 4. Chirp Z-Transform Fig 2: Oreration in CZT
  5. 5. Data Time WarpingDTW algorithm is based on Dynamic Programming techniques Fig. 3. A Warping between two time series
  6. 6. Experiment conditionProcess Description1) Speaker 3 Female 3 Male2) Tools Cool Edit Pro 2.0 tool3) Environment Laboratory4) Sampling Frequency, fs 300-3000 Hz4) Utterance Noisy area
  7. 7. RECOGNITIONTesting Testing Correct Percentage Testing Testing Correct PercentageSet Number Number % Set Number Number % 0 8 6 75 0 8 6 75 1 8 7 87.5 1 8 7 87.5 2 8 8 100 2 8 8 100 3 8 3 37.5 3 8 4 50 4 8 4 50 4 8 5 62.5 5 8 8 100 5 8 8 100 6 8 6 75 6 8 7 87.5 7 8 8 100 7 8 8 100 8 8 8 100 8 8 8 100 9 8 8 100 9 8 8 100 conditions:fl=300,fh=3000,M=256Table 1. Recognition Rate of the MFCC Table 2. Recognition Rate of the MFCC+CZTBased
  8. 8. Testing Testing Correct Percenta Set Number Number ge % 0 8 6 75 Cepstral MFCC MFCC&CZT- Coefficients based 1 8 7 87.5 2 8 8 100 Testing 80 80 Number 3 8 6 50 Correct 66 69 4 8 6 62.5 Number 5 8 8 100 Percentage / % 79.825 86.25 6 8 7 87.5 7 8 8 100 fl=300,fh=3000,M=256 8 8 8 100 Table 4. Different Cepstral Coefficients 9 8 8 100 conditions:fl=300,fh=3000,M=512Table 3. Recognition Rate of the MFCC+CZTBased
  9. 9. Conclusion The design and implementation of the experiment, we come tothe following conclusions, a new approach, called CZT-basedalgorithm, was developed to extract speech signals that are highlytransient in nature. We combine the CZTbased method with MFCC hasdemonstrated its superiority over the previously reported MFCCmethod in that the frequency resolution of the highly transientspeech signals is much enhanced, with better accuracy,widespread integration of speech recognition technology into end-user applications is ahead.
  10. 10. REFERENCES[1] L.R. Rabiner, B.Gold, in: Theory and Application of Digital Signal Processing,Prentice-Hail, Englewood Cliffs, NJ, 1975, p.393.[2] J.P. Openshaw, Z.P. Sun, J.S. Mason, "A comparison of composite featuresunder degraded speech in speaker recognition", Proceedings of the InternationalConference on Acoustics, Speech, and Signal Processing.[3] R. Vergin, D. O’Shaughnessy, V. Gupta, "Compensated mel frequencycepstrum coefficients", Proceedings of the International Conference on Acoustics,Speech, and Signal Processing.[4] Picone J W, "Signal modeling techniques in speech recognition", InProceedings of the IEEE,1993,81(9):1215- 1247.[5] Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient(MFCC) and Dynamic Time Warping (DTW) Techniques. Lindasalwa Muda,Mumtaj Begam and I. Elamvazuthi

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