CAJ | 학술논문

　　针对基于隐马尔科夫（HMM，Hidden Markov Model）的MAP和MMSE两种语音增强算法计算量大且前者不能处理非平稳噪声的问题，借鉴语音分离方法，提出了一种语音分离与HMM相结合的语音增强算法。该算法采用适合处理非平稳噪声的多状态多混合单元HMM，对带噪语音在语音模型和噪声模型下的混合状态进行解码，结合语音分离方法中的最大模型理论进行语音估计，避免了迭代过程和计算量特别大的公式计算，减少了计算复杂度。实验表明，该算法能够有效地去除平稳噪声和非平稳噪声，且感知评价指标PESQ 的得分有明显提高，算法时间也得到有效控制。
　　침대기우은마이과부（HMM，Hidden Markov Model）적MAP화MMSE량충어음증강산법계산량대차전자불능처리비평은조성적문제，차감어음분리방법，제출료일충어음분리여HMM상결합적어음증강산법。해산법채용괄합처리비평은조성적다상태다혼합단원HMM，대대조어음재어음모형화조성모형하적혼합상태진행해마，결합어음분리방법중적최대모형이론진행어음고계，피면료질대과정화계산량특별대적공식계산，감소료계산복잡도。실험표명，해산법능구유효지거제평은조성화비평은조성，차감지평개지표PESQ 적득분유명현제고，산법시간야득도유효공제。
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 computa-tional complexity, and the former can’t handle non-stationary noise. In response to these shortcomings, with the speech separa-tion 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 maxi-mization model theory which avoids iterative procedure and huge computation so that the complexity is reduced. The experi-ments 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.