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Hits 1 – 4 of 4
1
LIBRI-LIGHT: a benchmark for asr with limited or no supervision
Kahn, Jacob
;
Rivière, Morgane
;
Zheng, Weiyi
...
In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-02959460 ; 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⟩ (2020)
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2
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
Kharitonov, Eugene
;
Rivière, Morgane
;
Synnaeve, Gabriel
...
In: SLT 2020 - IEEE Spoken Language Technology Workshop ; https://hal.archives-ouvertes.fr/hal-03070321 ; SLT 2020 - IEEE Spoken Language Technology Workshop, Dec 2020, Shenzhen / Virtual, China (2020)
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3
Unsupervised pretraining transfers well across languages
Rivière, Morgane
;
Joulin, Armand
;
Mazaré, Pierre-Emmanuel
...
In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-02959418 ; ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona / Virtual, Spain. pp.7414-7418, ⟨10.1109/ICASSP40776.2020.9054548⟩ (2020)
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4
Unsupervised pretraining transfers well across languages ...
Rivière, Morgane
;
Joulin, Armand
;
Mazaré, Pierre-Emmanuel
;
Dupoux, Emmanuel
. - : arXiv, 2020
Abstract:
Cross-lingual and multi-lingual training of Automatic Speech Recognition (ASR) has been extensively investigated in the supervised setting. This assumes the existence of a parallel corpus of speech and orthographic transcriptions. Recently, contrastive predictive coding (CPC) algorithms have been proposed to pretrain ASR systems with unlabelled data. In this work, we investigate whether unsupervised pretraining transfers well across languages. We show that a slight modification of the CPC pretraining extracts features that transfer well to other languages, being on par or even outperforming supervised pretraining. This shows the potential of unsupervised methods for languages with few linguistic resources. ... : 6 pages. Accepted at ICASSP 2020. However the 2 pages of supplementary materials will appear only in the arxiv version ...
Keyword:
Audio and Speech Processing eess.AS
;
Computation and Language cs.CL
;
FOS Computer and information sciences
;
FOS Electrical engineering, electronic engineering, information engineering
;
Machine Learning cs.LG
;
Sound cs.SD
URL:
https://arxiv.org/abs/2002.02848
https://dx.doi.org/10.48550/arxiv.2002.02848
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