Generation of fundamental frequency contours for Thai speech synthesis using tone nucleus model

In Thai language, stress is an important prosodic feature that not only affects naturalness but also has a crucial role in meaning of phrase-level utterance. It is seen that a speech synthesis model that is trained with lack of stress and phrase-level information causes incorrect tones and ambiguity in meaning of synthetic speech. Our previous work has shown that manually annotated stress information improves naturalness of synthetic speech. However, a high time consumption is a drawback of the manual annotation. In this paper, we utilize an unsupervised learning technique called Bayesian Gaussian process latent variable model (Bayesian GP-LVM) to automatically put stress annotation on the given training data. Stress related features are projected onto a latent space in which syllables are easier classified into stressed/unstressed classes. We use the stressed/unstressed information as an additional context in GPR-based speech synthesis. Experimental results show that the proposed technique improves naturalness of synthetic speech as well as accuracy of stressed/unstressed classification. Moreover, the proposed technique enables us to avoid ambiguity in meaning of synthetic speech by providing intended stress position into context label sequence to be synthesized.

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Unsupervised Stress Information Labeling Using Gaussian Process Latent Variable Model for Statistical Speech Synthesis

Moungsri

Koriyama

Kobayashi

2016

Interspeech 2016

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Tone modeling using Gaussian process latent variable model for statistical speech synthesis

Moungsri¹,

Koriyama²,

Kobayashi³

2016

Speech Prosody 2016

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In continuous speech of Thai language, tone pronunciation is affected by several factors. One of significant factors is stress that causes a diversity of F0 contours of tone, and affects syllable durations. Our previous studies have shown that a stressed/unstressed syllable context improves tone modeling accuracy. However, the stress in Thai language is generally unknown for a given input text and it has a wide variety of degrees of stress. Thus the simple stressed/unstressed context is insufficient to represent variation of stress. In this study, we introduce an unsupervised dimensional reduction technique, variational GP-LVM, to represent a diversity of stress. The stress-related information, F0 contour and duration, is projected onto a latent space which has lower dimensionality than the original to represent the variation of stress. Then, we use the latent variable as a stress-related context in GPR-based speech synthesis framework that enables us to determine the similarity of contextual factors continuously using a kernel function. We examine two approaches to data projection: single-space projection and separated-space projection. Objective and subjective evaluation results show that the proposed technique achieves an improvement in tone modeling.

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Generation of fundamental frequency contours for Thai speech synthesis using tone nucleus model

Cited by 2 publications

References 26 publications

Unsupervised Stress Information Labeling Using Gaussian Process Latent Variable Model for Statistical Speech Synthesis

Unsupervised Stress Information Labeling Using Gaussian Process Latent Variable Model for Statistical Speech Synthesis

Tone modeling using Gaussian process latent variable model for statistical speech synthesis

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