Browsing by Author "Lee, Chin-Hui"

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A STUDY ON CROSS-LANGUAGE KNOWLEDGE INTEGRATION IN MANDARIN LVCSR
(2012)
Chiang, Chen-Yu
;
Siniscalchi, Sabato Marco
;
Wang, Yih-Ru
;
Chen, Sin-Horng
;
Lee, Chin-Hui
We present a cross-language knowledge integration framework to improve the performance in large vocabulary continuous speech recognition. Two types of knowledge sources, manner attribute and prosodic structure, are incorporated. For manner of articulation, cross-lingual attribute detectors trained with an American English corpus (WSJ0) are utilized to verify and rescore hypothesized Mandarin syllables in word lattices obtained with state-of-the-art systems. For the prosodic structure, models trained with an unsupervised joint prosody labeling and modeling technique using a Mandarin corpus (TCC300) are used in lattice rescoring. Experimental results on Mandarin syllable, character and word recognition with the TCC300 corpus show that the proposed approach significantly outperforms the baseline system that does not use articulatory and prosodic information. It also demonstrates a potential of utilizing results from cross-lingual attribute detectors as a language-universal frontend for automatic speech recognition.
Knowledge Integration for Improving Performance in LVCSR
(2013-01-01)
Chiang, Chen-Yu
;
Siniscalchi, Sabato Marco
;
Chen, Sin-Horng
;
Lee, Chin-Hui
;
電機工程學系
This paper presents a knowledge integration framework to improve performance in large vocabulary continuous speech recognition. Two types of knowledge sources, manner attribute and prosodic structure, are incorporated. For manner of articulation, six attribute detectors trained with an American English corpus (WSJO) are utilized to rescore hypothesized phones in word lattices obtained by a baseline ASR system. For the prosodic structure, models trained with an unsupervised joint prosody labeling and modeling (PLM) technique using WSJO are used in lattice rescoring. Experimental results on the American English WSJ word recognition task of the Nov92 test set show that the proposed approach significantly outperforms the baseline system that does not use articulatory and prosodic information. The results also demonstrate the effectiveness and usefulness of the PLM technique in constructing prosodic models for American English ASR.