A time continuous model for speech recognition

In this paper we present a time continuous extension of the hidden Markov model approach in order to obtain a better representation of the continuous nature of the speech process. The discrete state sequence of the hidden Markov model is replaced by a continuous parameter, varying between 0 and 1. For an utterance and a given word model an optimum mapping of the feature vectors to the continuous axis is found and the likelihood is calculated based on this mapping. As a first test of this very general approach we extended the hidden Markov model by first mapping the states onto the new axis. Values between the states are then obtained by interpolation between the states. As alternatives we considered interpolation of either the likelihood values of the state density functions and/or of the parameters of the density functions itself. The approach was tested in a speaker independent isolated word recognition system.