Abstract
Automatic speech recognition for languages in Southeast Asia, including Chinese, Thai and Vietnamese, typically models both acoustics and languages at the syllable level. This paper presents a new approach for recognizing those languages by exploiting information at the word level. The new approach, adapted from our FLaVoR architecture[1], consists of two layers. In the first layer, a pure acoustic-phonemic search generates a dense phoneme network enriched with meta data. In the second layer, a word decoding is performed in the composition of a series of finite state transducers (FST), combining various knowledge sources across sub-lexical, word lexical and word-based language models. Experimental results on the Vietnamese Broadcast News corpus showed that our approach is both effective and flexible.
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© 2006 Springer-Verlag Berlin Heidelberg
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Vu, Q., Demuynck, K., Van Compernolle, D. (2006). Vietnamese Automatic Speech Recognition: The FLaVoR Approach. In: Huo, Q., Ma, B., Chng, ES., Li, H. (eds) Chinese Spoken Language Processing. ISCSLP 2006. Lecture Notes in Computer Science(), vol 4274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11939993_49
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DOI: https://doi.org/10.1007/11939993_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49665-6
Online ISBN: 978-3-540-49666-3
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