Stochastic modeling of temporal information in speech for hidden Markov models

A Markov chain, namely, the temporal Markov model, is used to model the time-ordering information of the feature vectors of a spoken word. An empirical method is suggested to combine the temporal Markov model (TMM) with the hidden Markov model (HMM) for word recognition. Experiments on speaker-independent isolated English alphabet recognition showed that this method is effective in terms of improved recognition.