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Communication Dans Un Congrès Année : 2020

LIBRI-LIGHT: a benchmark for asr with limited or no supervision

Résumé

We introduce a new collection of spoken English audio suitable for training speech recognition systems under limited or no supervision. It is derived from open-source audio books from the LibriVox project. It contains over 60K hours of audio , which is, to our knowledge, the largest freely-available corpus of speech. The audio has been segmented using voice activity detection and is tagged with SNR, speaker ID and genre descriptions. Additionally, we provide baseline systems and evaluation metrics working under three settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi-supervised setting (PER, CER) and (3) the distant supervision setting (WER). Settings (2) and (3) use limited textual resources (10 minutes to 10 hours) aligned with the speech. Setting (3) uses large amounts of unaligned text. They are evaluated on the standard LibriSpeech dev and test sets for comparison with the supervised state-of-the-art. Index Terms-unsupervised and semi-supervised learning , distant supervision, dataset, zero-and low resource ASR.
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Dates et versions

hal-02959460 , version 1 (06-10-2020)

Identifiants

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Jacob Kahn, Morgane Rivière, Weiyi Zheng, Eugene Kharitonov, Qiantong Xu, et al.. LIBRI-LIGHT: a benchmark for asr with limited or no supervision. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona / Virtual, Spain. pp.7669-7673, ⟨10.1109/ICASSP40776.2020.9052942⟩. ⟨hal-02959460⟩
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