# J-MAC: Japanese multi-speaker audiobook corpus for speech synthesis

arXiv: https://arxiv.org/abs/2201.10896 · HTML: https://mlsanity.com/paper/2201.10896/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2022.

## Built on by (most cited first, 4 of 4)

- [Do learned speech symbols follow Zipf's law?](https://mlsanity.com/paper/2309.09690/) (2023)
- [A Pilot Study of GSLM-based Simulation of Foreign Accentuation Only Using Native Speech Corpora](https://mlsanity.com/paper/2407.11370/) (2024)
- [Analytic Study of Text-Free Speech Synthesis for Raw Audio using a Self-Supervised Learning Model](https://mlsanity.com/paper/2412.03074/) (2024)
- [MixedG2P-T5: G2P-free Speech Synthesis for Mixed-script texts using Speech Self-Supervised Learning and Language Model](https://mlsanity.com/paper/2509.01391/) (2025)

## Abstract (arXiv)

In this paper, we construct a Japanese audiobook speech corpus called "J-MAC" for speech synthesis research. With the success of reading-style speech synthesis, the research target is shifting to tasks that use complicated contexts. Audiobook speech synthesis is a good example that requires cross-sentence, expressiveness, etc. Unlike reading-style speech, speaker-specific expressiveness in audiobook speech also becomes the context. To enhance this research, we propose a method of constructing a corpus from audiobooks read by professional speakers. From many audiobooks and their texts, our method can automatically extract and refine the data without any language dependency. Specifically, we use vocal-instrumental separation to extract clean data, connectionist temporal classification to roughly align text and audio, and voice activity detection to refine the alignment. J-MAC is open-sourced in our project page. We also conduct audiobook speech synthesis evaluations, and the results give insights into audiobook speech synthesis.