4th International Conference on Spoken Language Processing
Philadelphia, PA, USA
This paper presents a multi-strategic and hybrid approach for large-scale integrated speech and natural language processing, employing connectionist, statistical and symbolic techniques. The developed spoken Korean processing engine (SKOPE) integrates connectionist TDNN-based phoneme recognition technique with statistical Viterbi-based lexical decoding and symbolic morphological/phonological analysis techniques. The modular large-scale TDNNs are organized to recognize all 41 Korean phonemes using 10 component networks combined through 3 glue networks. In performance phase, continuously shifted TDNN outputs are integrated with HMM-based Viterbi decoding using a tree-structured lexicon. The Viterbi beam search is integrated with Korean morphotactics and phonological modeling, and produces a morpheme-graph for high-level parsing module. Currently, SKOPE shows average 76.2% phoneme spotting performance for all 41 Korean phonemes (including silence) from continuous speech signals and exhibits average 92.6% morpheme spotting performance from erroneous TDNN outputs after morphological analysis. Other extensive experiments verify that the multi-strategic approaches are promising for complex integrated speech and natural language processing, and the approaches can be extended to other morphologically complex agglutinative languages such as Japanese.
Bibliographic reference. Lee, Geunbae / Lee, Jong-Hyeok / Park, Kyubong / Kim, Byung-Chang (1996): "Integrating connectionist, statistical and symbolic approaches for continuous spoken Korean processing", In ICSLP-1996, 458-461.