关键词: 语音增强, 语音分离, 非平稳噪声, 基于HMM的语音增强

Abstract: There are two typical speech enhancement algorithms based on HMM（Hidden Markov Model） which are MAP（Maximum A Posteriori） estimator and MMSE（Minimum Mean-Square Error） estimator. Both algorithms have high computational complexity, and the former can’t handle non-stationary noise. In response to these shortcomings, with the speech separation technology as reference, speech enhancement algorithm based on speech separation using HMM is designed. This algorithm uses the multi-state AR-HMM which is applied to non-stationary noise condition to decode the mixed state sequence of noisy speech under the speech model and noise model. Then, the decoded speech is estimated by speech separation method using maximization model theory which avoids iterative procedure and huge computation so that the complexity is reduced. The experiments also show that the proposed algorithm can effectively remove the stationary noise and non-stationary noise, improve the PESQ（Perceptual Evaluation of Speech Quality） score and the algorithm time is under control too.

Key words: speech enhancement, speech separation, non-stationary noise, speech enhancement based on HMM