Speech & Audio
NLP
Libri-light: A benchmark for ASR with limited or no supervision
May 4, 2020
Abstract
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 semisupervised 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.
AUTHORS
Written by
Jacob Kahn
Morgan Rivière
Weiyi Zheng
Evgeny Kharitonov
Qiantong Xu
Pierre-Emmanuel Mazaré
Julien Karadayi
Vitaliy Liptchinsky
Ronan Collobert
Tatiana Likhomanenko
Armand Joulin
Abdelrahman Mohamed
Emmanuel Dupoux
Publisher
International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
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