A theory on optimal construction of dynamic features of speech for HMM-based speech recognition

The construction of dynamic (delta) features of speech, which has been in the past confined to the pre-processing domain in hidden Markov modelling (HMM), is generalized and formulated as an integrated speech modeling problem. This generalization allows to utilize state-dependent weights to transform static speech features into dynamic ones. The author describes a rigorous theoretical framework that naturally incorporates the generalized dynamic-parameter technique, and presents a maximum-likelihood based algorithm for integrated optimization of the conventional HMM parameters and of the time-varying weighting functions that define the dynamic features of speech.<
>