Evaluation of segmental unit input HMM

The standard HMM cannot fully express the time variant features while staying at the same state. So as not to ignore the dynamic changes of the speech characteristics, various methods have been studied. In this paper, we compare a segmental unit input HMM where several successive frames are combined and become an input vector, with conditional density HMM or the use of regression coefficients and evaluate them. Using segmental statistics, since the dimension of the parameters increases, results in a lesser precision in estimation of the covariance matrix. Therefore we used methods for compressing dimension and reducing computation by K-L expansion and MQDF. By segmental unit inputting for the basic structure HMM, we got a better recognition rate than by traditional methods and the combination of a segmental unit of successive mel-cepstrum frames and regression coefficients showed the best recognition rate.