Mixture Gaussian HMM-trajctory method using likelihood compensation

We propose a new speech recognition method (HMM-trajectory method) that generates a speech trajectory from HMMs by maximizing their likelihood while accounting for the relationship between the MFCCs and dynamic MFCCs. One major advantage of this method is that this relationship, ignored in conventional speech recognition, is directly used in the speech recognition phase. This paper improves the recognition performance of the HMM-trajectory method for dealing with mixture Gaussian distributions. While the HMM-trajectory method chooses the Gaussian distribution sequence of the HMM states by selecting the best Gaussian distribution in the state during Viterbi decoding and calculating HMM trajectory likelihood along with the sequence, the proposed method compensates for HMM trajectory likelihood using ordinary HMM likelihood. In speaker-independent speech recognition experiments, the proposed method reduced the error rate about 10% for the task compared with HMMs, proving its effectiveness for Gaussian mixture components.