Acoustic model-based subword tokenization and prosodic-context extraction without language knowledge for text-to-speech synthesis

Abstract

This paper presents text tokenization and context extraction without using language knowledge for text-to-speech (TTS) synthesis. To generate prosody, statistical parametric TTS synthesis typically requires the professional knowledge of the target language. Therefore, languages suitable for TTS synthesis are limited to only rich-resource languages. To achieve TTS synthesis without using language knowledge, we propose acoustic model-based subword tokenization and unsupervised extraction of prosodic contexts. The subword tokenization can determine language units suitable for prosody generation. The context extraction can retrieve contexts from pairs of subwords and prosody. The proposed methods function without language knowledge and can improve F0 prediction accuracy. Experimental evaluation demonstrates that 1) the training of proposed subword tokenization, which uses the expectation-maximization algorithm and deep neural networks, is empirically stable, 2) the proposed subword tokenization tokenizes text into subwords that are close to language-specific units, and 3) the proposed methods outperform the conventional methods using language model-based tokenization in terms of synthetic speech quality.