High-quality audio-visual corpora are essential for building voice agents capable of natural human-machine communication, but existing corpora commonly contain a limited amount of data per speaker, making personalized modeling difficult. We present CAVIARES, a new audio-visual corpus comprising 9.5 hours of expressive speech recorded by a single professional Japanese female speaker. CAVIARES consists of two subsets: acted dialogue and expressive reading, providing a diverse range of speaking styles for speech-to-facial motion modeling and multimodal learning tasks. In this paper, we describe the construction process of CAVIARES and the results of corpus analysis. CAVIARES will be released for research purposes only.

We propose a novel phrase break prediction method that combines implicit features extracted from a pre-trained large language model, a.k.a BERT, and explicit features extracted from BiLSTM with linguistic features. In conventional BiLSTM based methods, word representations and/or sentence representations are used as independent components. The proposed method takes account of both representations to extract the latent semantics, which cannot be captured by previous methods. The objective evaluation results show that the proposed method obtains an absolute improvement of 3.2 points for the F1 score compared with BiLSTM-based conventional methods using linguistic features. Moreover, the perceptual listening test results verify that a TTS system that applied our proposed method achieved a mean opinion score of 4.39 in prosody naturalness, which is highly competitive with the score of 4.37 for synthesized speech with ground-truth phrase breaks.