Comparison of discrete and continuous HMMs in a CSR task over the telephone

Attention is given to a comparison of the performance of discrete and continuous density hidden Markov models (DDHMMs and CDHMMs) on a 786-word E-mail inquiry task performed by the speaker-independent word recognition component of a speech understanding system. This comparison between DDHMMs and CDHMMs has also been carried out by training speaker-dependent models. The authors also present the results of a set of experiments carried out with the aim at automatically selecting a suitable set of subword unit models by a clustering procedure. The recognizer gives word accuracy (WA) rates of 67.8% and 75.3% by using DDHMMs and CDHMMs, respectively, without any linguistic constraints. On the same task, WA rates of 87.1% and 85.9% have been obtained in the speaker-dependent mode.