Chairman:Hung-Chi Yang Presenter: Yue -Fong Guo Advisor: Dr. Yeou-Jiunn Chen Date: 2013.3.20

1. Classification of place of articulation in unvoiced stops with spectro-temporal surface modeling V. Karjigi, P. RaoDept. of Electrical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India Received 8 December 2011; received in revised form 12 March 2012; accepted 23 April 2012 Available online 1 June 2012 Chairman:Hung-Chi YangPresenter: Yue-Fong Guo Advisor: Dr. Yeou-Jiunn ChenDate: 2013.3.20

2. Outline • Introduction • MFCC • 2D-DCT • Polynomial surface

3. Outline • GMM • Results • Conclusion

4. Introduction • Automatic speech recognition (ASR) system • The goal is the lexical content of the human voice is converted to a computer-readable input • Attempt to identify or confirm issue voice speaker rather than the content of the terms contained therein

5. Introduction • Automatic speech recognition (ASR) system • Acoustics feature • Signal processing and feature extraction • Mel frequency cepstral coefficients (MFCC) • Acoustics model • Statistically speech model • Gaussian mixture model (GMM)

6. MFCC • Mel frequency cepstral coefficients (MFCC) • MFCC takes human perception sensitivity with respect to frequencies into consideration, and therefore are best for speech/speaker recognition.

7. MFCC • Pre-emphasis • The speech signal s(n) is sent to a high-pass filter • Frame blocking • Hamming windowing • Each frame has to be multiplied with a hamming window in order to keep the continuity of the first and the last points in the frame

8. MFCC • Fast Fourier Transform or FFT • The time domain signal into a frequency domain • Triangular BandpassFilters • Smooth the magnitude spectrum such that the harmonics are flattened in order to obtain the envelop of the spectrum with harmonics. • Discrete cosine transform or DCT

9. MFCC • Log energy • The energy within a frame is also an important feature that can be easily obtained • Delta cepstrum • Actually used in speech recognition, we usually coupled differential cepstrum parameters to show the changes of the the cepstrum parameters of the time

10. 2D-DCT • 2D-DCT modeling

11. Polynomial surface • Polynomial surface modeling

12. Polynomial surface • Polynomial surface modeling

13. Polynomial surface • Polynomial surface modeling

14. Polynomial surface • Polynomial surface modeling

15. GMM • Gaussian mixture model (GMM) • Is an effective tool for data modeling and pattern classification • Speaker acoustic characteristics for clustering, and then each group of acoustic characteristics described with a Gaussian density distribution

16. Databases • Databases • Evaluated on two distinct datasets • American English continuous speech as provided in the TIMIT database • Marathi words database specially created for the purpose

17. Results

18. Conclusion • A comparison of performance with published results on the same task revealed that the spectro-temporal feature systems tested in this work improve upon the best previous systems’ performances in terms of classification accuracies on the specified datasets.

19. The End