Previously, 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. We also demonstrated a phonetic recognizer and a word-spotter based on ANGIE. In this work, we extend ANGIE to a competitive continuous speech recognition system. Furthermore, given that ANGIE is based on a context-free framework, we have decided to combine ANGIE with TINA, a context-free based framework for natural language understanding, into an integrated system. The integration led to a 21.7% reduction in word error rate compared to a baseline word bigram recognizer on ATIS. We also examined the addition of new words to the vocabulary, an area we believe will benefit from both ANGIE and the ANGIE-plus-TINA integration. The combination reduced error rate by 20.8% over the baseline and outperformed several other configurations tested not involving an integrated ANGIE-plus-TINA.
Cite as: Lau, R., Seneff, S. (1998) A unified framework for sublexical and linguistic modelling supporting flexible vocabulary speech understanding. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 0053, doi: 10.21437/ICSLP.1998-652
@inproceedings{lau98_icslp,
author={Raymond Lau and Stephanie Seneff},
title={{A unified framework for sublexical and linguistic modelling supporting flexible vocabulary speech understanding}},
year=1998,
booktitle={Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998)},
pages={paper 0053},
doi={10.21437/ICSLP.1998-652}
}