Abstract
Tone plays an important lexical role in spoken tonal languages like Mandarin Chinese. In this paper we propose a two-pass search strategy for improving tonal syllable recognition performance. In the first pass, instantaneous F0 information is employed along with corresponding cepstral information in a 2-stream HMM based decoding. The F0 stream, which incorporates both discrete voiced/unvoiced information and continuous F0 contour, is modeled with a multi-space distribution. With just the first-pass decoding, we recently reported a relative improvement of 24% reduction of tonal syllable recognition errors on a Mandarin Chinese database [5]. In the second pass, F0 information over a horizontal, longer time span is used to build explicit tone models for rescoring the lattice generated in the first pass. Experimental results on the same Mandarin database show that an additional 8% relative error reduction of tonal syllable recognition is obtained by the second-pass search, lattice rescoring with enhanced tone models.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, H., Qian, Y., Soong, F., Zhou, JL., Han, J. (2006). Improved Mandarin Speech Recognition by Lattice Rescoring with Enhanced Tone Models. 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_47
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DOI: https://doi.org/10.1007/11939993_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49665-6
Online ISBN: 978-3-540-49666-3
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