# JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis

arXiv: https://arxiv.org/abs/1711.00354 · HTML: https://mlsanity.com/paper/1711.00354/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2017.

## Built on by (most cited first, 30 of 58)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [WaveFake: A Data Set to Facilitate Audio Deepfake Detection](https://mlsanity.com/paper/2111.02813/) (2021)
- [ESPnet2-TTS: Extending the Edge of TTS Research](https://mlsanity.com/paper/2110.07840/) (2021)
- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset](https://mlsanity.com/paper/2409.08731/) (2024)
- [Talking Face Generation with Multilingual TTS](https://mlsanity.com/paper/2205.06421/) (2022)
- [Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretraining](https://mlsanity.com/paper/2301.07295/) (2023)
- [A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks](https://mlsanity.com/paper/2305.11073/) (2023)
- [Integrating Pre-Trained Speech and Language Models for End-to-End Speech Recognition](https://mlsanity.com/paper/2312.03668/) (2023)
- [Towards Interactive Intelligence for Digital Humans](https://mlsanity.com/paper/2512.13674/) (2025)
- [Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection](https://mlsanity.com/paper/2206.13979/) (2022)
- [SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection](https://mlsanity.com/paper/2210.06105/) (2022)
- [Multi-Dataset Co-Training with Sharpness-Aware Optimization for Audio Anti-spoofing](https://mlsanity.com/paper/2305.19953/) (2023)
- [LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation](https://mlsanity.com/paper/2502.20583/) (2025)
- [The DeepSpeak Dataset](https://mlsanity.com/paper/2408.05366/) (2024)
- [RefineGAN: Universally Generating Waveform Better than Ground Truth with Highly Accurate Pitch and Intensity Responses](https://mlsanity.com/paper/2111.00962/) (2021)
- [InterBiasing: Boost Unseen Word Recognition through Biasing Intermediate Predictions](https://mlsanity.com/paper/2406.14890/) (2024)
- [SaMoye: Zero-shot Singing Voice Conversion Model Based on Feature Disentanglement and Enhancement](https://mlsanity.com/paper/2407.07728/) (2024)
- [VoiceWukong: Benchmarking Deepfake Voice Detection](https://mlsanity.com/paper/2409.06348/) (2024)
- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [Decoupling Speaker-Independent Emotions for Voice Conversion Via Source-Filter Networks](https://mlsanity.com/paper/2110.01164/) (2021)
- [Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study](https://mlsanity.com/paper/2301.09099/) (2023)
- [Wave-U-Net Discriminator: Fast and Lightweight Discriminator for Generative Adversarial Network-Based Speech Synthesis](https://mlsanity.com/paper/2303.13909/) (2023)
- [Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models](https://mlsanity.com/paper/2305.05201/) (2023)
- [Voice Cloning: Comprehensive Survey](https://mlsanity.com/paper/2505.00579/) (2025)
- [LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context](https://mlsanity.com/paper/2505.17410/) (2025)
- [Towards Natural and Controllable Cross-Lingual Voice Conversion Based on Neural TTS Model and Phonetic Posteriorgram](https://mlsanity.com/paper/2102.01991/) (2021)
- [Lightweight Model Attribution and Detection of Synthetic Speech via Audio Residual Fingerprints](https://mlsanity.com/paper/2411.14013/) (2024)
- [WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing](https://mlsanity.com/paper/2506.01263/) (2025)
- [Sarashina2.2-TTS: Tackling Kanji Polyphony in Japanese Speech Generation via Data Scaling and Targeted Data Synthesis](https://mlsanity.com/paper/2606.25369/) (2026)

## Abstract (arXiv)

Thanks to improvements in machine learning techniques including deep learning, a free large-scale speech corpus that can be shared between academic institutions and commercial companies has an important role. However, such a corpus for Japanese speech synthesis does not exist. In this paper, we designed a novel Japanese speech corpus, named the "JSUT corpus," that is aimed at achieving end-to-end speech synthesis. The corpus consists of 10 hours of reading-style speech data and its transcription and covers all of the main pronunciations of daily-use Japanese characters. In this paper, we describe how we designed and analyzed the corpus. The corpus is freely available online.