Duration modelling in finite state automata for speech recognition and fast speaker adaptation

The classical first-order Hidden Markov Models with continuous probabilistic density function (HMMCs) seem to be a promising tool for speech modelling with reference to the task of both isolated word and continuous speech recognition. However, these models have a strong limitation because they are poorly able to capture the information about duration, sometimes the most important feature that permits to distinguish between similar sounds. In this paper two different approaches are exploited, in such a way to obtain sets of models in which the state duration is characterized by suited probability density functions. In order to evaluate the performance of both model sets, two difficult speaker-dependent recognition tasks have been carried out. It has been also tested the opportunity of using a limited-size training lexicon for a new speaker, and merge these duration models with the other ones obtained through some speakers.