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The Accented English Speech Recognition Challenge 2020: Open Datasets, Tracks, Baselines, Results and Methods

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arxiv 2102.10233 v1 pith:KQKRLL35 submitted 2021-02-20 cs.SD eess.AS

The Accented English Speech Recognition Challenge 2020: Open Datasets, Tracks, Baselines, Results and Methods

Xian Shi , Fan Yu , Yizhou Lu , Yuhao Liang , Qiangze Feng , Daliang Wang , Yanmin Qian , Lei Xie This is my paper
classification cs.SD eess.AS
keywords speechchallengeenglishrecognitionaccentedtrackreleasedaccents
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The variety of accents has posed a big challenge to speech recognition. The Accented English Speech Recognition Challenge (AESRC2020) is designed for providing a common testbed and promoting accent-related research. Two tracks are set in the challenge -- English accent recognition (track 1) and accented English speech recognition (track 2). A set of 160 hours of accented English speech collected from 8 countries is released with labels as the training set. Another 20 hours of speech without labels is later released as the test set, including two unseen accents from another two countries used to test the model generalization ability in track 2. We also provide baseline systems for the participants. This paper first reviews the released dataset, track setups, baselines and then summarizes the challenge results and major techniques used in the submissions.

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Cited by 2 Pith papers

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  1. Few-Shot Synthetic Accented Speech for ASR Fine-Tuning: What Helps and When?

    cs.SD 2026-04 unverdicted novelty 5.0

    Few-shot TTS adaptation combined with LLM-guided phoneme editing produces synthetic accented speech that improves ASR word error rates on real accented audio even in cross-speaker and ultra-low-data settings.

  2. Few-Shot Synthetic Accented Speech for ASR Fine-Tuning: What Helps and When?

    cs.SD 2026-04 unverdicted novelty 5.0

    Random phoneme substitutions recover most ASR gains from synthetic accented speech, with targeted edits and ground-truth prosody providing only marginal additional benefits.