Recognition method with parametric trajectory generated from mixture distribution HMMs

We have proposed a new speech recognition technique that generates a speech trajectory from HMMs by maximizing the likelihood of the trajectory, while accounting for the relation between the cepstrum and the dynamic cepstrum coefficients. This method has the major advantage that the relation, which is ignored in conventional speech recognition, is directly used in the speech recognition phase. This paper describes an extension of the method for dealing with HMMs whose distributions are mixture Gaussian distributions. The method chooses the sequence of Gaussian distributions by selecting the best Gaussian distribution in the state during Viterbi decoding. Speaker-independent speech recognition experiments were carried out. The proposed method obtained an 18.2% reduction in error rate for the task, proving that the proposed method is effective even for Gaussian mixture HMMs.