Autoregressive Hidden Markov Model and the Speech Signal

This paper introduces an autoregressive hidden Markov model (HMM) and demonstrates its application to the speech signal. In this variant of the HMM the observed signal is assumed to be Gaussian autoregressive and the probability density function is derived based on an approximation of the linear prediction error. A Baum-Welch style set of re-estimation formulas are then derived and used to infer the model parameters for a given data set, which correspond to linguistic structure in the context of speech data. The new set of re-estimation formulas are then applied to speech data and experimental results demonstrate inference of broad phonetic categories without prior knowledge of linguistic information. The experimental results and stability of this model are then briefly contrasted with historic experiments wherein phonetic information has been inferred directly from the speech signal using a similar autoregressive model.