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Integrated speech and morphological processing in a connectionist continuous speech understanding for Korean
A new tightly coupled speech and natural language integration model is
presented for a TDNN-based continuous possibly large vocabulary speech
recognition system for Korean. Unlike popular n-best techniques developed for
integrating mainly HMM-based speech recognition and natural language processing
in a {\em word level}, which is obviously inadequate for morphologically
complex agglutinative languages, our model constructs a spoken language system
based on a {\em morpheme-level} speech and language integration. With this
integration scheme, the spoken Korean processing engine (SKOPE) is designed and
implemented using a TDNN-based diphone recognition module integrated with a
Viterbi-based lexical decoding and symbolic phonological/morphological
co-analysis. Our experiment results show that the speaker-dependent continuous
{\em eojeol} (Korean word) recognition and integrated morphological analysis
can be achieved with over 80.6% success rate directly from speech inputs for
the middle-level vocabularies.Comment: latex source with a4 style, 15 pages, to be published in computer
processing of oriental language journa
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