一种改进的隐马尔可夫模型在语音识别中的应用

提出了一种新的马尔可夫模型——异步隐马尔可夫模型．该模型针对噪音环境下语音识别过程中出现丢失帧的情况，通过增加新的隐藏时间标示变量Ck，估计出实际观察值对应的状态序列，实现对不规则或者不完整采样数据的建模．详细介绍了适合异步HMM的前后向算法以及用于训练的EM算法，并且对转移矩阵的计算进行了优化．最后通过实验仿真，分别使用经典HMM和异步HMM对相同的随机抽取帧的语音数据进行识别,识别结果显示在抽取帧相同情况下异步HMM比经典HMM的识别错误率低．

Application of an Improved HMM to Speech Recognition

A new Markov model,i.e.,asynchronous HMM(hidden Markov model) is proposed.By adding a new hidden time-stamp variable Ck,the presented model can be applied to the situations in which some frames are lost in the process of speech recognition to estimate the state sequence corresponding with the actual observation value,and can model the irregularly or incompletely sampled data.The forwards/backwards algorithm as well as EM(Expectation-Maximization) training algorithm which are suitable for asynchronous HMM(AHMM) are particularly introduced,and the calculation of transition matrix is optimized.At last,traditional HMM and AHMM models are used in experiment and simulation to recognize the same speech data of which the frame blocks are randomly extracted,and the results show that the recognition error rate of AHMM is lower than that of traditional HMM in condition of extracting the same amount of frame block.