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Robust Speech Enhancement Using HMM and $H_\infty$ Filter  

이기용 (숭실대학교 정보통신전자공학부)
김준일 (숭실대학교 정보통신과)
Publication Information
The Journal of the Acoustical Society of Korea / v.23, no.7, 2004 , pp. 540-547 More about this Journal
Abstract
Since speech enhancement algorithms based on Kalman/Wiener filter require a priori knowledge of the noise and have focused on the minimization of the variance of the estimation error between clean and estimated speech signal, small estimation error on the noise statistics may lead to large estimation error. However, H/sub ∞/ filter does not require any assumptions and a priori knowledge of the noise statistics, but searches the best estimated signal among the entire estimated signal by applying least upper bound, consequently it is more robust to the variation of noise statistics than Kalman/Wiener filter. In this paper, we Propose a speech enhancement method using HMM and multi H/sub ∞/ filters. First, HMM parameters are estimated with the training data. Secondly, speech is filtered with multiple number of H/sub ∞/ filters. Finally, the estimation of clean speech is obtained from the sum of the weighted filtered outputs. Experimental results shows about 1dB∼2dB SNR improvement with a slight increment of computation compared with the Kalman filter method.
Keywords
Speech Enhancement; Kalman filter; $H_\infty$filter; HMM; EM algorithm;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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