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Báo cáo hóa học: Time-Varying Noise Estimation for Speech Enhancement and Recognition Using Sequential Monte Carlo Method

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: 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: Time-Varying Noise Estimation for Speech Enhancement and Recognition Using Sequential Monte Carlo Method | EURASIP Journal on Applied Signal Processing 2004 15 2366-2384 2004 Hindawi Publishing Corporation Time-Varying Noise Estimation for Speech Enhancement and Recognition Using Sequential Monte Carlo Method Kaisheng Yao Institute for Neural Computation University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0523 USA Email kyao@ucsd.edu Te-Won Lee Institute for Neural Computation University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0523 USA Email tewon@ucsd.edu Received 4 May 2003 Revised 9 April 2004 We present a method for sequentially estimating time-varying noise parameters. Noise parameters are sequences of time-varying mean vectors representing the noise power in the log-spectral domain. The proposed sequential Monte Carlo method generates a set of particles in compliance with the prior distribution given by clean speech models. The noise parameters in this model evolve according to random walk functions and the model uses extended Kalman filters to update the weight of each particle as a function of observed noisy speech signals speech model parameters and the evolved noise parameters in each particle. Finally the updated noise parameter is obtained by means of minimum mean square error MMSE estimation on these particles. For efficient computations the residual resampling and Metropolis-Hastings smoothing are used. The proposed sequential estimation method is applied to noisy speech recognition and speech enhancement under strongly time-varying noise conditions. In both scenarios this method outperforms some alternative methods. Keywords and phrases sequential Monte Carlo method speech enhancement speech recognition Kalman filter robust speech recognition. 1. INTRODUCTION A speech processing system maybe required to work in conditions where the speech signals are distorted due to background noise. Those distortions can drastically drop the performance of automatic speech recognition ASR systems which usually perform well in quiet