Speaker adaptation of speaking rate-dependent hierarchical prosodic model for Mandarin TTS

Po-Chun Wang, I-Bin Liao, Chen-Yu Chiang, Yih-Ru Wang, Sin-Horng Chen
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引用次数: 8

Abstract

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.
基于语速的汉语TTS分层韵律模型的说话人自适应
本文提出了一种说话人自适应方法,将现有的基于语速的分层韵律模型(SR-HPM)应用于SR-HPM控制的普通话TTS系统的新说话人数据,从而实现新的语音。本文主要解决了两个问题:仅在正常语速小范围内存在少量自适应语音的数据稀疏性问题,以及在快语速和慢语速两个范围内都没有自适应数据的问题。该方法遵循SR-HPM训练的思想,首先对新说话者语音数据的韵律声学特征进行归一化,然后通过韵律标记和建模算法对HPM进行训练,最后将HPM细化为sr依赖模型。采用模型参数外推的MAP自适应方法来解决上述两个问题。对男性说话人自适应数据的实验结果证实,所得到的自适应SR- hpm具有合理的参数,覆盖了较宽的说话速率范围,因此可以在TTS系统中用于生成韵律声学特征,以合成任何给定SR的新说话人的声音。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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