Loosely coupled HMMs for ASR

Hidden Markov Models (HMMs) have been successful for modelling the dynamics of carefully dictated speech, but their performance degrades severely when used to model conversational speech. This paper presents a preliminary feasibility study of an alternative class of models: loosely coupled HMMs. Since speech is produced by a system of loosely coupled articulators, stochastic models explicitly representing this parallelism may have advantages for automatic speech recognition (ASR), particularly when trying to model the phonological effects inherent in casual spontaneous speech. The paper evaluates one coupled model on a simple ASR task, using both exact and approximate estimation schemes. We conclude such models merit further investigation.