ANGIE: A new framework for speech analysis based on morpho-phonological modelling

Abstract

This paper describes a new system for speech analysis, ANGIE, which characterizes word substructure in terms of a trainable grammar. ANGIE capture morpho-phonemic and phonological phenomena through a hierarchical framework. The terminal categories can be alternately letters or phone units, yielding a reversible letter-tosound/sound-to-letter system. In conjunction with a segment network and acoustic phone models, the systemcan produce phonemicto-phonetic alignments for speech waveforms. For speech recognition, ANGIE uses a one-pass bottom-up best-first search strategy. Evaluated in the ATIS domain, ANGIEachieveda phone error rate of 36%, as compared with 40 % achieved with a baseline phone-bigram based recognizer under similar conditions. ANGIEpotentially offers many attractive features, including dynamic vocabulary adaptation, as well as a framework for handling unknown words

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