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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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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.