Browsing by Author "Liao, I-Bin"

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Hierarchical Prosody Modeling of English Speech and its Application to TTS
(2014-01-01)
Tsai, Chung-Yao
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Kuo, Chin-Kuan
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Wang, Yih-Ru
;
Chen, Sin-Horng
;
Liao, I-Bin
In this paper, a hierarchical prosody modeling approach for English speech is proposed. It is an extended version of the HPM approach proposed previously for Mandarin speech. It first designs a syllable-based, statistical prosodic model to describe various relationships of prosodic-acoustic features of the speech signal, linguistic features of the associated text, and prosodic tags representing the underlining prosody structure of the speech. It then employs a prosody labeling and modeling algorithm to estimate the model parameters and label the prosodic tags of all training utterances simultaneously from a prosody-unlabeled speech corpus. Experimental results on a corpus containing many paragraphic utterances of a female English-majored Chinese speaker show that the inferred parameters of the model are all meaningful. We then use the trained model to generate prosodic information for a TTS system. An informal listening test shows that the synthetic speech sounds quite natural.
Speaker Adaptation of Speaking Rate-dependent Hierarchical Prosodic Model for Mandarin TTS
(2014-01-01)
Wang, Po-Chun
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Liao, I-Bin
;
Chiang, Chen-Yu
;
Wang, Yih-Ru
;
Chen, Sin-Horng
In this paper, a speaker adaptation method to adapt an existing speaking rate-dependent hierarchical prosodic model (SR-HPM) of an SR-controlled Mandarin TTS system to new speaker's data for realizing a new voice is proposed. Two main problems are addressed: data sparseness for few adaptation utterances existing only in a small range of normal speaking rate and no adaptation data in both ranges of fast and slow speaking rates. The proposed method follows the idea of SR-HPM training to firstly normalize the prosodic-acoustic features of the new speaker's speech data, to then train an HPM by the prosody labeling and modeling algorithm, and to lastly refine the HPM to an SR-dependent model. The MAP adaptation method with model parameter extrapolation is applied to cope with the above two problems. Experimental results on a male speaker's adaptation data confirmed that the resulting adaptive SR-HPM has reasonable parameters covering a wide range of speaking rates and hence can be used in the TTS system to generate prosodic-acoustic features for synthesizing the new speaker's voice of any given SR.
Speaker Adaptation of SR-HPM for Speaking Rate-Controlled Mandarin TTS
(2016-11)
Liao, I-Bin
;
Chiang, Chen-Yu
;
Wang, Yih-Ru
;
Chen, Sin-Horng
;
交大名義發表
In this paper, a structural maximum a posteriori (SMAP) speaker adaptation approach to adjusting the speaking rate (SR)-dependent hierarchical prosodic model (SR-HPM) of an existing SR-controlled Mandarin text-to-speech system to a new speaker\'s data for producing a new voice is discussed. Two main issues are addressed. One is the small SR coverage of the adaptation data and is solved by using the existing SR-HPM that was trained from a speech corpus of wide SR coverage as an informative prior. Another is the data sparseness problem resulting from the large number of parameters of the SR-HPM to be adjusted. It is solved by hierarchically organizing the SR-HPM parameters into decision trees so as to be efficiently adjusted by the SMAP method. The effectiveness of the proposed approach is evaluated on speech databases of five new speakers. Both objective and subjective evaluations show that the proposed method not only performs better than the maximum likelihood-based method in the observed SR range of the target speaker\'s data, but also is much better in the unseen SR ranges.
STRUCTURAL MAXIMUM A POSTERIORI SPEAKER ADAPTATION OF SPEAKING RATE-DEPENDENT HIERARCHICAL PROSODIC MODEL FOR MANDARIN TTS
(2016)
Liao, I-Bin
;
Chiang, Chen-Yu
;
Chen, Sin-Horng
;
電機學院
;
College of Electrical and Computer Engineering
In this paper, a structural maximum a posterior speaker adaptation method to adjust the existing speaking rate (SR) dependent hierarchical prosodic model (SR-HPM) to a new speaker\'s data for realizing a new voice of any given SR is discussed. The adaptive SR-HPM is formulated based on MAP estimation with a reference SR-HPM serving as an informative prior. The prior information provided by the reference SR-HPM is hierarchically organized by decision trees. The results of objective and subjective evaluations showed that the proposed method not only performed slightly better than the maximum likelihood-based model in the observed SR range of the target speaker\'s data, but also was much better in the unseen SR range.