基于PMC方法的鲁棒声学模型研究

Noise Robust Acoustic Model Research Based on PMC

在噪声鲁棒语音识别研究中,使用并行模型结合(parallel model combination,PMC)方法得到的模型理论上能够接近匹配噪声环境模型的性能,故成为噪声鲁棒语音识别的重要研究方向.本文首先提出了一种基于前后向差分动态参数的特征MFCC—FWD—BWD,该特征满足PMC对特征构造矩阵可逆的要求.在此基础上,提出了一种用于PMC的新模型———并行子状态隐马尔可夫模型(parallel sub-state hidden Markov model,PSSHMM),该模型每个状态包含平行关系的子状态,且子状态间存在转移关系.实验表明,PSSHMM模型在各种噪声和SNR下取得了较好的识别效果,特别是对于非平稳噪声,其鲁棒性能非常显著.

In noise robust speech recognition, for PMC（parallel model combination） method, the performance of the combined model can approach that of the model matching the noisy environment theoretically, so it is an important noise robust speech recognition research field. In this paper, a novel feature MFCC FWD BWD, which is based on forward-backward difference dynamic parameters, is presented to satisfy the requirement that the feature construction matrix is invertible for PMC. Based on this condition, a novel structure model named parallel sub-state hidden Markov model （PSSHMM） is presented for PMC and each state of this model has parallel sub-states with transitions. In experiments, PSSHMM achieves good results under each kind of noise and each level of SNR, especially for non-stationary noise, its robust performance is also excellent.