A stochastic approach to phoneme and accent estimation

We present a new stochastic approach to estimate accurately phonemes and accents for Japanese TTS (Text-to-Speech) systems. Front-end process of TTS system assigns phonemes and accents to an input plain text, which is critical for creating intelligible and natural speech. Rule-based approaches that build hierarchical structures are widely used for this purpose. However, considering scalability and the ease of domain adaptation, rule-based approaches have well-known limitations. In this paper, we present a stochastic method based on an n-gram model for phonemes and accents estimation. The proposed method estimates not only phonemes and accents but word segmentation and part-of-speech (POS) simultaneously. We implemented a system for Japanese which solves tokenization, linguistic annotation, text-to-phonemes conversion, homograph disambiguation, and accents generation at the same time, and observed promising results.