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 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
