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THCHS-30 : A Free Chinese Speech Corpus

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arxiv 1512.01882 v2 pith:WSKNHQUH submitted 2015-12-07 cs.CL cs.SD

Dong Wang , Xuewei Zhang This is my paper · ORCID
classification cs.CLcs.SD
keywords speechdataresearchchinesefreerecognitiondatabaseinstitutes
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation

    cs.CL 2025-09 conditional novelty 6.0 of 10

    PARCO cuts named-entity errors by large margins (NE-CER 1.57% on AISHELL-1, NE-WER 8.34% on DATA2 at zero distractors) by combining phoneme-enriched entity encoding, a contrastive disambiguation loss, and hierarchical...

  2. Mel-McNet: A Mel-Scale Framework for Online Multichannel Speech Enhancement

    eess.AS 2025-05 conditional novelty 6.0 of 10

    Mel-McNet performs online multichannel speech enhancement in the Mel domain, reducing FLOPs by roughly 60% versus McNet while keeping speech quality and ASR accuracy comparable.

  3. Speech-Driven End-to-End Language Discrimination towards Chinese Dialects

    cs.CL 2026-06 unverdicted novelty 4.0 of 10

    A speech-driven pipeline with MFCC features, HMM-DNN speech recognition, attention, and CNN fusion is presented for fine-grained Chinese dialect discrimination and evaluated on two benchmark corpora.

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