| 摘要: |
| 在使用传统的隐马尔可夫模型（traditional hidden Markov model,简称THMM）刻画现实中的语音时有一个明显的缺点,即THMM不能合适地表征语音信号的时域结构.时域上的相关性被认为对识别非常有用,因为相邻帧间的特征矢量具有很强的相关性.文章提出了一种新的方法，用以把时域的相关性糅合到一个基于传统的隐马尔可夫模型的语音识别系统中.首先,用条件概率的形式处理帧间相关性;然后,用一种非线性的概率近似公式来表征相邻帧之间的相关性.此方法丝毫不增加原来的THMM的空间复杂度,而且也几乎不增 |
| 关键词: 连续隐马尔可夫模型,帧间相关性,非线性估计,混合高斯密度,联合条件概率密度. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家摼盼鍞攻关项目基金资助. |
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| A New Method in Hidden Markov Model for Modeling Frame Correlation |
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GUO Qing,WU Wen-hu,FANG Di-tang
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| Abstract: |
| In this paper, the authors present a novel method to incorporate temporal correlation into a speech recognition system based on conventional hidden Markov model (HMM). The temporal correlation is considered to be useful for recognition because of the fact that the speech features of the present frame are highly informative about the feature characteristics of neighboring frames. An obvious way to incorporate temporal correlation is to condition the probability of the current observation on the current state as well as on the previous observation and the previous state. But using this method directly must lead to unreliable parameter estimation for the number of parameters to be estimated may increase too excessively to limited train data. In this paper, the authors approximate the joint conditional PD by non-linear estimation method. As a result, they can still use mixture Gaussian density to represent the joint conditional PD for the principle of any PD can be approximated by mixture Gaussian density. The HMM incorporated temporal correlation by non-linear estimation method, which they called FC (frame correlation) HMM does not need any additional parameters and it only brings a little additional computing quantity. The results of the experiment show that the top 1 recognition rate of FC HMM has been raised by 6 percent compared to the conventional HMM method. |
| Key words: Continuous hidden Markov model (CHMM), frame correlation, non-linear estimation, mixture Gaussian density, joint conditional probability density. |
