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A unified framework for sublexical and linguistic modelling supporting flexible vocabulary speech understanding

In [9], we introduced the ANGIE framework for modelling speech where morphological and phonological substructures of words are jointly characterized by a context-free grammar and represented in a multi-layered hierarchical structure. In [6], we demonstrated a competitive word-spotter based on the ANGIE framework and presented several results comparing the performance of various sublexical filler models. In the present work, completed as a part of [5], we extend the ANGIE framework to a competitive full continuous speech recognition system. Furthermore, given that ANGIE is based on a context-free framework, we have decided to combine ANGIE with TINA ([8]), a contextfree based framework for natural language understanding, into an integrated system. The integrated system led to a 21.7% reduction in word error rate compared to a baseline word bigram recognizer on ATIS. Numerous issues relating to the construction of the combined system were explored. We have also examined the addition of new words to the recognizer vocabulary, one of the areas which we believe will benefit from the ANGIE framework and also from the ANGIE-plus-TINA integration. Our combined system achieved an error rate reduction of 20.8% over the baseline system and outperformed several other configurations we tested not involving an integrated ANGIE-plus-TINA.