一种DHMM的混合训练方法

隐马尔柯夫模型(HMM)作为描述语音信号的一个工具,按输出概率分布的不同,可分为连续HMM(CHMM)和离散HMM(DHMM).经典的训练方法Baum-Welch算法虽然收敛迅速,但是这类基于爬山的算法只能取得局部最优解,从而影响了系统的识别率.对于CHMM,借助于分类K平均方法可以取得可靠的初始点以保证迅速准确的收敛.而对于DHMM,该方法收益不大,最终所得的仍是局部最优解.由于进化计算一个最重要的特点便是全局搜索,这样可得全局最优解或次优解.本文将进化计算应用到DHMM的训练中,提出了一个把传统算法和进化计算相结合的混合算法.实验结果表明该方法既保证了全局搜索又实现了快速收敛,最终所得的模型优于传统方法和简单进化计算方法.

A Hybrid Training Method for DHMMs

Hidden Markov Models are very successful in modeling the acoustic behavior of speech.They may be classified into two groups,continuous HMMs (CHMMs) and discrete HMMs (DHMMs),according to the output probability distribution.Traditional training methods such as Baum Welch algorithm are noted for the rapid convergence.However,these methods are hill climbing based algorithms and they just lead to locally optimal solutions,which might deteriorate the recognition rate.For CHMMs,a segmental k means method has been developed to get reliable initial estimate and thus guaranteed the rapid and proper convergence.For DHMMs,this offers little help and the final solution is a locally optimal solution.While one outstanding character of evolutionary computation is global search,it can converge to a globally optimal solution or at least a sub optimal solution.In this paper evolutionary computation is applied to training DHMMs.A hybrid training method that combines the traditional method and evolutionary computation is proposed.Experimental results show that the proposed method has both qualities of global search and rapid convergence and the resulting models are superior to those obtained with traditional methods or simple evolutionary computation and eventually contribute to the increase of recognition rate.