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

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-to-sound/sound-to-letter system. In conjunction with a segment network and acoustic phone models, the system can produce phonemic-to-phonetic alignments for speech waveforms. For speech recognition, ANGIE uses a one-pass bottom-up best-first search strategy. Evaluated in the ATIS domain, ANGIE achieved a phone error rate of 36%, as compared with 40% achieved with a baseline phone-bigram based recognizer under similar conditions. ANGIE potentially offers many attractive features, including dynamic vocabulary adaptation, as well as a framework for handling unknown words.