# WenetSpeech-Yue: A Large-scale Cantonese Speech Corpus with Multi-dimensional Annotation

arXiv: https://arxiv.org/abs/2509.03959 · HTML: https://mlsanity.com/paper/2509.03959/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2025.

## Built on by (most cited first, 9 of 9)

- [Qwen3-ASR Technical Report](https://mlsanity.com/paper/2601.21337/) (2026)
- [OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models](https://mlsanity.com/paper/2604.00688/) (2026)
- [WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus with Rich Annotation for Dialectal Speech Processing](https://mlsanity.com/paper/2509.18004/) (2025)
- [NIM4-ASR: Towards Efficient, Robust, and Customizable Real-Time LLM-Based ASR](https://mlsanity.com/paper/2604.18105/) (2026)
- [PolySpeech-100: A Large-Scale Benchmark for Speech Understanding Across 100+ Languages and Dialects](https://mlsanity.com/paper/2606.01016/) (2026)
- [Language-Specialized Multi-Teacher On-Policy Distillation for Multilingual LLM-Based ASR](https://mlsanity.com/paper/2608.03610/) (2026)
- [Imitation Learning for Elder-Facing Speech Synthesis](https://mlsanity.com/paper/2606.21053/) (2026)
- [DialectS2S: End-to-End Speech Dialogue Modeling for Low-Resource Chinese Dialects](https://mlsanity.com/paper/2608.08067/) (2026)
- [On-Policy Self-Distillation for Multi-Dialect ASR: Mastering Dialects, Retaining Mandarin](https://mlsanity.com/paper/2608.11898/) (2026)

## Abstract (arXiv)

The development of speech understanding and generation has been significantly accelerated by the availability of large-scale, high-quality speech datasets. Among these, ASR and TTS are regarded as the most established and fundamental tasks. However, for Cantonese (Yue Chinese), spoken by approximately 84.9 million native speakers worldwide, limited annotated resources have hindered progress and resulted in suboptimal ASR and TTS performance. To address this challenge, we propose WenetSpeech-Pipe, an integrated pipeline for building large-scale speech corpus with multi-dimensional annotation tailored for speech understanding and generation. It comprises six modules: Audio Collection, Speaker Attributes Annotation, Speech Quality Annotation, Automatic Speech Recognition, Text Postprocessing and Recognizer Output Voting, enabling rich and high-quality annotations. Based on this pipeline, we release WenetSpeech-Yue, the first large-scale Cantonese speech corpus with multi-dimensional annotation for ASR and TTS, covering 21,800 hours across 10 domains with annotations including ASR transcription, text confidence, speaker identity, age, gender, speech quality scores, among other annotations. We also release WSYue-eval, a comprehensive Cantonese benchmark with two components: WSYue-ASR-eval, a manually annotated set for evaluating ASR on short and long utterances, code-switching, and diverse acoustic conditions, and WSYue-TTS-eval, with base and coverage subsets for standard and generalization testing. Experimental results show that models trained on WenetSpeech-Yue achieve competitive results against state-of-the-art (SOTA) Cantonese ASR and TTS systems, including commercial and LLM-based models, highlighting the value of our dataset and pipeline.