An new method used in HMM for modeling frame correlation

Abstract

We present a novel method to incorporate temporal correlation into a speech recognition system based on conventional hidden Markov model (HMM). In this new model the probability of the current observation not only depends on the current state but also depends on the previous state and the previous observation. The joint conditional probability density (PD) is approximated by a non-linear estimation method. As a result, we can still use the mixture Gaussian density to represent the joint conditional PD for the principle of any PD can be approximated by the mixture Gaussian density. The HMM incorporated temporal correlation by the non-linear estimation method, which we called FC 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.