Browsing by Author "You, Chung-Long"

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Hierarchical prosody modeling for Mandarin spontaneous speech
(2019-04-01)
Lin, Cheng-Hsien
;
You, Chung-Long
;
Chiang, Chen-Yu
;
Wang, Yih-Ru
;
Chen, Sin-Horng
In this paper, a hierarchical prosody model (HPM)-based method for Mandarin spontaneous speech is proposed. First, an HPM is designed for describing relations among acoustic features of utterances, linguistic features of texts, and prosodic tags representing the underlying hierarchical prosodic structures of utterances. Subsequently, a sequential optimization algorithm is employed to train the HPM based on a large conversational speech corpus, the Mandarin Conversational Dialogue Corpus (MCDC), which features orthographic transcriptions and prosodic event annotations. In this unsupervised training method, all utterances of the MCDC are labeled with two types of prosodic tags, namely, break and prosodic states, automatically and simultaneously. After training, the HPM parameters are examined to identify critical prosodic properties of Mandarin spontaneous speech, which are then compared with their counterparts in the read-speech HPM. The prosodic tags on the studied utterances enable mapping of various prosodic events onto the hierarchical prosodic structures of the utterances. Prosodic analyses of some disfluent events are conducted using the prosodic tags affixed to the MCDC. Finally, an application of the HPM to assist in Mandarin spontaneous-speech recognition is discussed. Significant relative error rate reductions of 9.0%, 9.2%, 15.6%, and 7.3% are obtained for base-syllable, character, tone, and word recognition, respectively. (C) 2019 Acoustical Society of America.
Rich Prosodic Information Exploration on Spontaneous Mandarin Speech
(2016-01-01)
Lin, Cheng-Hsien
;
You, Chung-Long
;
Chiang, Chen-Yu
;
Wang, Yih-Ru
;
Chen, Sin-Horng
In this paper, rich prosodic information of spontaneous Mandarin speech is explored. The joint prosody labeling and modeling algorithm proposed previously for read speech is extended to spontaneous-speech prosody modeling by additionally considering the modeling of disfluency speech parts. It trains a hierarchical prosodic model and performs prosody labeling from a large speech corpus automatically. Rich prosodic information is then explored via analyzing model parameters and labeling results. By comparing the resulting prosodic model with that of read speech, we find that most affecting patterns, such as F0 contour patterns of 4 tones, have similar shapes or same trends but with much less dynamic ranges. Besides, the prosodic characteristics of various disfluency events, including repetition, restart, repair, contraction, and hesitation, are intensively investigated based on the labeling results. The information explored increases our knowledge about the phonology of spontaneous speech, and should be useful for assisting in ASR.