# THCHS-30 : A Free Chinese Speech Corpus

arXiv: https://arxiv.org/abs/1512.01882 · HTML: https://mlsanity.com/paper/1512.01882/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2015.

## Built on by (most cited first, 30 of 43)

- [mHuBERT-147: A Compact Multilingual HuBERT Model](https://mlsanity.com/paper/2406.06371/) (2024)
- [Interspeech 2021 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2101.01902/) (2021)
- [Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset](https://mlsanity.com/paper/2203.16844/) (2022)
- [Encoding of lexical tone in self-supervised models of spoken language](https://mlsanity.com/paper/2403.16865/) (2024)
- [BART based semantic correction for Mandarin automatic speech recognition system](https://mlsanity.com/paper/2104.05507/) (2021)
- [ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark](https://mlsanity.com/paper/2507.05727/) (2025)
- [Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt](https://mlsanity.com/paper/2403.11780/) (2024)
- [FLM-Audio: Natural Monologues Improves Native Full-Duplex Chatbots via Dual Training](https://mlsanity.com/paper/2509.02521/) (2025)
- [Disentangleing Content and Fine-grained Prosody Information via Hybrid ASR Bottleneck Features for Voice Conversion](https://mlsanity.com/paper/2203.12813/) (2022)
- [MSR-86K: An Evolving, Multilingual Corpus with 86,300 Hours of Transcribed Audio for Speech Recognition Research](https://mlsanity.com/paper/2406.18301/) (2024)
- [USTC-NELSLIP System Description for DIHARD-III Challenge](https://mlsanity.com/paper/2103.10661/) (2021)
- [Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition](https://mlsanity.com/paper/2204.03855/) (2022)
- [ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction](https://mlsanity.com/paper/2412.03075/) (2024)
- [Improving speech recognition models with small samples for air traffic control systems](https://mlsanity.com/paper/2102.08015/) (2021)
- [Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems](https://mlsanity.com/paper/2104.01287/) (2021)
- [A Novel Speech-Driven Lip-Sync Model with CNN and LSTM](https://mlsanity.com/paper/2205.00916/) (2022)
- [Analyzing the relationships between pretraining language, phonetic, tonal, and speaker information in self-supervised speech models](https://mlsanity.com/paper/2506.10855/) (2025)
- [Oriental Language Recognition (OLR) 2020: Summary and Analysis](https://mlsanity.com/paper/2107.05365/) (2021)
- [Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge](https://mlsanity.com/paper/2205.07083/) (2022)
- [PERT: A New Solution to Pinyin to Character Conversion Task](https://mlsanity.com/paper/2205.11737/) (2022)
- [Wideband Audio Waveform Evaluation Networks: Efficient, Accurate Estimation of Speech Qualities](https://mlsanity.com/paper/2206.13272/) (2022)
- [Improved Factorized Neural Transducer Model For text-only Domain Adaptation](https://mlsanity.com/paper/2309.09524/) (2023)
- [CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR](https://mlsanity.com/paper/2502.20040/) (2025)
- [Inclusivity of AI Speech in Healthcare: A Decade Look Back](https://mlsanity.com/paper/2505.10596/) (2025)
- [FCPE: A Fast Context-based Pitch Estimation Model](https://mlsanity.com/paper/2509.15140/) (2025)
- [Word-Free Spoken Language Understanding for Mandarin-Chinese](https://mlsanity.com/paper/2107.00186/) (2021)
- [Audio Deepfake Attribution: An Initial Dataset and Investigation](https://mlsanity.com/paper/2208.10489/) (2022)
- [Adversarial Data Augmentation for Robust Speaker Verification](https://mlsanity.com/paper/2402.02699/) (2024)
- [Multi-Accent Mandarin Dry-Vocal Singing Dataset: Benchmark for Singing Accent Recognition](https://mlsanity.com/paper/2512.07005/) (2025)
- [Full Attention Bidirectional Deep Learning Structure for Single Channel Speech Enhancement](https://mlsanity.com/paper/2108.12105/) (2021)

## Abstract (arXiv)

Speech data is crucially important for speech recognition research. There are quite some speech databases that can be purchased at prices that are reasonable for most research institutes. However, for young people who just start research activities or those who just gain initial interest in this direction, the cost for data is still an annoying barrier. We support the `free data' movement in speech recognition: research institutes (particularly supported by public funds) publish their data freely so that new researchers can obtain sufficient data to kick of their career. In this paper, we follow this trend and release a free Chinese speech database THCHS-30 that can be used to build a full- edged Chinese speech recognition system. We report the baseline system established with this database, including the performance under highly noisy conditions.