Linear predictive hidden Markov models and the speech signal

A method for modelling time series is presented and then applied to the analysis of the speech signal. A time series is represented as a sample sequence generated by a finite state hidden Markov model with output densities parameterized by linear prediction polynomials and error variances. These objects are defined and their properties developed. The theory culminates in a theorem that provides a computationally efficient iterative scheme to improve the model. The theorem has been used to create models from speech signals of considerable length. One such model is examined with emphasis on the relationship between states of the model and traditional classes of speech events. A use of the method is illustrated by an application to the talker verification problem.