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Classification of place of articulation in unvoiced stops with spectro-temporal surface modeling V. Karjigi , P. Rao Dept. 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 Yang Presenter: Yue-Fong Guo Advisor: Dr. Yeou-Jiunn Chen Date: 2013.3.20 Outline • Introduction • MFCC • 2D-DCT • Polynomial surface Outline • GMM • Results • Conclusion 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 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) 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. MFCC 1. Pre-emphasis • The speech signal s(n) is sent to a high-pass filter 2. Frame blocking 3. 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 MFCC 4. Fast Fourier Transform or FFT • The time domain signal into a frequency domain 5. Triangular Bandpass Filters • Smooth the magnitude spectrum such that the harmonics are flattened in order to obtain the envelop of the spectrum with harmonics. 6. Discrete cosine transform or DCT MFCC 7. Log energy • The energy within a frame is also an important feature that can be easily obtained 8. 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 2D-DCT • 2D-DCT modeling Polynomial surface • Polynomial surface modeling Polynomial surface • Polynomial surface modeling Polynomial surface • Polynomial surface modeling Polynomial surface • Polynomial surface modeling 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 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 Results 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. The End