Stochastic signal models represent a powerful way to approach the problem of speech recognition. A particular stochastic modeling, the first order Hidden Markov Model (HMM), has become increasingly popular, because it has a solid theoretical basis and offers practical advantages. In this paper we will extend the standard HMM theory to Parallel Hidden Markov Model (PHMM). The parallel model consists of two statistically related HMMs. This configuration permits a more complete and accurate characterization of the speech signal. In this framework, an observation consists of a couple of acoustic parameter vectors, one for a standard HMM and the other for an HMM whose parameters are probabilistic functions of the state of the first model.
Bibliographic reference. Brugnara, F. / Mori, Renato De / Giuliani, D. / Omologo, M. (1991): "A parallel HMM approach to speech recognition", In EUROSPEECH-1991, 1103-1106.