Mora-Level Prosody Prediction for Text-to-Speech Using Japanese BERT Without Accentual Labels

In practical text-to-speech (TTS) for pitch accent languages, such as Japanese, high-fidelity synthesis with correct prosody requires not only a phoneme sequence but also accentual information. Although accentual information can be obtained from accent dictionaries, words not included in the dictionaries and accent sandhi are sometimes synthesized with incorrect prosody, and manual registration of huge amounts of accent data is costly. Additionally, previous machine learning-based data-driven accent information estimation approaches for TTS also require huge quantities of handcrafted accentual labels during training. This paper proposes a data-driven prosody prediction method for Japanese TTS that uses Japanese BERT and does not require any accentual labels during training. A Japanese TTS acoustic model with mora-level (katakana sequence) input is first trained and mora-level fundamental frequency values (f<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf>), which directly correspond to the prosody, are extracted for the training data using forced alignment. Then, a pre-trained Japanese BERT is finetuned for the mora-level f<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> prediction task with word sequences including kanji and the corresponding katakana sequences as input and the mora-level f<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> extracted using forced alignment as the prediction target. During TTS inference, the mora-level f<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> sequence predicted by the finetuned Japanese BERT is input to the TTS acoustic model along with the katakana input, and correct prosodic synthesis can be realized thanks to this predicted f<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> sequence. Experimental results demonstrate that the proposed method can realize the same synthesis quality and higher accent correctness compared with conventional neural TTS models with accentual labels.