Robust speech recognition system using multi-parameter bidirectional Kalman filter

Robust speech recognition system using multi-parameter bidirectional Kalman filter A speech signal processing system using multi-parameter model bidirectional Kalman filter has been proposed in this paper. Conventional unidirectional Kalman filter usually performs estimation of current state speech signal by processing the time varying autoregressive model of speech signals from the past time states. A bidirectional Kalman filter utilizes the past and future measurements to estimate the current state of a speech signal that minimize the mean of the squared error using efficient recursive means. The matrices involved in the difference equations and the measurement equations of the bidirectional Kalman filter algorithm are kept constant throughout the process. With multi-parameter model, the proposed bidirectional Kalman filter relates more measurements from the future and past time states to the current time state. The proposed multi-parameter bidirectional Kalman filter has been implemented into a speech recognition system and its performance has been compared to other conventional speech processing algorithms. Compared to the single-parameter model bidirectional Kalman filter, the multi-parameter bidirectional Kalman filter improves the accuracy in the state prediction, reduces the speech information lost after the filtering process and better word error rate has been achieved at high SNR regions (clean, 20, 15, 10 dB). International Journal of Speech Technology Springer Journals

Robust speech recognition system using multi-parameter bidirectional Kalman filter

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Springer US
Copyright © 2017 by Springer Science+Business Media New York
Engineering; Signal,Image and Speech Processing; Social Sciences, general; Artificial Intelligence (incl. Robotics)
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