Revisiting autoregressive hidden Markov modeling of speech signals

Linear predictive hidden Markov modeling is compared with a simple form of the switching autoregressive process. The latter process captures existing signal correlation during transitions of the Markov chain. Parameter estimation is described using naturally stable forward-backward recursions. The switching autoregressive model outperformed the linear predictive model in a digit recognition task and provided comparable performance to a cepstral-based recognizer.