Non-linear probability estimation method used in HMM for modeling frame correlation

Abstract

In this paper we present a novel method to incorporate temporal correlation into a speech recognition system based on HMM. 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 use this method directly must lead to unreliable parameter estimates for the number of parameters to be estimated may increase too excessively to limited train data. In this paper, we approximate the joint conditional PD by non-linear estimation method. The HMM incorporated temporal correlation by non-linear estimation method, which we called it FC HMM does not need any additional parameters and it only brings a little additional computing quantity. The results in the experiment show that the top 1 recognition rate of FC HMM has been raised by 6 percent compared to the traditional HMM method.