Experimental evaluation of duration modelling techniques for automatic speech recognition

This paper presents an experimental evaluation of two such extensions: hidden semi-Markov models (HSMMs), and expanded state HMMs (ESHMMs). These extensions to the standard HMM (hiden Markov model) formalism permit improved duration modelling and experimental results are presented which show that they can consistently lead to improved performance. The results indicate that if sufficient training material is available, the best performance is obtained with the Fergusson model, but that with smaller training sets Poisson HSMMs or type B ESHMMs are more robust models.