Modeling acoustic transitions in speech by state-interpolation hidden Markov models

The authors present a new type of hidden Markov model (HMM) for vowel-to-consonant (VC) and consonant-to-vowel (CV) transitions based on the locus theory of speech perception. The parameters of the model can be trained automatically using the Baum-Welch algorithm and the training procedure does not require that instances of all possible CV and VC pairs be present. When incorporated into an isolated word recognizer with a 75000 word vocabulary it leads to the modest improvement in recognition rates. The authors give recognition results for the state interpolation HMM and compare them to those obtained by standard context-independent HMMs and generalized triphone models