Multi-linear HMM based system for articulatory feature extraction

Abstract

A novel system for automatic articulatory feature extraction has been developed. The system defines an autosegmental multi-linear representation of features and uses multiple hidden Markov model based recognisers to extract these feature classes. Overlap and precedence relations among features on different tiers can be extracted and then presented to a phonological parser for further recognition. The system thus accounts for coarticulation phenomena. The system was implemented using a novel modification of the HTK (HMM toolkit) which allows it to perform multi-thread multi-feature recognition. The system performance is extremely promising. Among the highest accuracies achieved are 98% for vowels and 93% for rhotic sounds. Current work investigates interdependencies of extracting different feature types.