A Phonological Modeling System Based on Autosegmental and Articulatory Phonology

This paper describes the design and implementation of a phonological modeling system based on autosegmental and articulatory phonology and its application in speech recognition. Pronunciation modeling is an integral part in speech recognition systems. Together with language modeling, signal processing and learning models (e.g. Hidden Markov model and neural network model), it innuences the performance of a speech recognition system. The basic units adopted by most speech recognition systems for word pronunciation modeling are linearly ordered diphones or triphones (i.e. context-dependent segments without internal structure). Our approach decomposes segments into articulatory features and allows these features to spread freely into neighbouring segments to model continuous speech. We use a feature-based nite-state transducer as the metalanguage for writing phonological rules. A set of rules has been implemented in this language. The writing of these rules are based on theories of autosegmental and artic-ulatory phonology. The numerical components of the rules are deduced from articulator trajec-tory data. The output of this system is gestural scores that model phonological alternations in continuous speech. The gestural scores are used to generate Hidden Markov Models for building speech recognition systems.