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Báo cáo hóa học: " Research Article A Maximum Likelihood Estimation of Vocal-Tract-Related Filter Characteristics for Single Channel Speech Separation"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Maximum Likelihood Estimation of Vocal-Tract-Related Filter Characteristics for Single Channel Speech Separation | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2007 Article ID 84186 15 pages doi 10.1155 2007 84186 Research Article A Maximum Likelihood Estimation of Vocal-Tract-Related Filter Characteristics for Single Channel Speech Separation Mohammad H. Radfar 1 Richard M. Dansereau 2 and Abolghasem Sayadiyan1 1 Department of Electrical Engineering Amirkabir University Tehran 15875-4413 Iran 2 Department of Systems and Computer Engineering Carleton University Ottawa ON Canada K1S 5B6 Received 3 March 2006 Revised 13 September 2006 Accepted 27 September 2006 Recommended by Lin-Shan Lee We present a new technique for separating two speech signals from a single recording. The proposed method bridges the gap between underdetermined blind source separation techniques and those techniques that model the human auditory system that is computational auditory scene analysis CASA . For this purpose we decompose the speech signal into the excitation signal and the vocal-tract-related filter and then estimate the components from the mixed speech using a hybrid model. We first express the probability density function PDF of the mixed speech s log spectral vectors in terms of the PDFs of the underlying speech signal s vocal-tract-related filters. Then the mean vectors of PDFs of the vocal-tract-related filters are obtained using a maximum likelihood estimator given the mixed signal. Finally the estimated vocal-tract-related filters along with the extracted fundamental frequencies are used to reconstruct estimates of the individual speech signals. The proposed technique effectively adds vocaltract-related filter characteristics as a new cue to CASA models using a new grouping technique based on an underdetermined blind source separation. We compare our model with both an underdetermined blind source separation and a CASA method. The experimental results show that our model outperforms both techniques in terms of SNR improvement and the percentage