A family of parallel hidden Markov models
Fabio Brugnara, Renato De Mori, Diego Giuliani, Maurizio Omologo · 1992
Stochastic signal models represent a powerful tool for automatic speech recognition. A particular type of stochastic modeling based on first-order hidden Markov models (HMMs), has been increasingly popular, because it has a solid theoretical basis and offers practical advantages. The authors extend the standard HMM theory to parallel hidden Markov models (PHMMs). The parallel model consists of two statistically related HMMs. This configuration has mixture densities of HMM observations whose weights can be made variable depending on the probability of other HMMs being in certain states. This allows one to dynamically adapt observation statistics to acoustic contexts. Some preliminary experiments have been carried out in order to compare the PHMMs with standard HMMs and the results are presented.>