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- W1569651595 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-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." @default.
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- W1569651595 date "2002-12-24" @default.
- W1569651595 modified "2023-10-14" @default.
- W1569651595 title "ANGIE: a new framework for speech analysis based on morpho-phonological modelling" @default.
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- W1569651595 doi "https://doi.org/10.1109/icslp.1996.607049" @default.
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