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1
Investigating alignment interpretability for low-resource NMT
In: ISSN: 0922-6567 ; EISSN: 1573-0573 ; Machine Translation ; https://hal.archives-ouvertes.fr/hal-03139744 ; Machine Translation, Springer Verlag, 2021, ⟨10.1007/s10590-020-09254-w⟩ (2021)
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2
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
In: INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021)
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LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
In: INTERSPEECH 2021: ; INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021)
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4
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
In: INTERSPEECH 2021: ; INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021)
Abstract: International audience ; Self-Supervised Learning (SSL) using huge unlabeled data has been successfully explored for image and natural language processing. Recent works also investigated SSL from speech. They were notably successful to improve performance on downstream tasks such as automatic speech recognition (ASR). While these works suggest it is possible to reduce dependence on labeled data for building efficient speech systems, their evaluation was mostly made on ASR and using multiple and heterogeneous experimental settings (most of them for English). This questions the objective comparison of SSL approaches and the evaluation of their impact on building speech systems. In this paper, we propose LeBenchmark: a reproducible framework for assessing SSL from speech. It not only includes ASR (high and low resource) tasks but also spoken language understanding, speech translation and emotion recognition. We also focus on speech technologies in a language different than English: French. SSL models of different sizes are trained from carefully sourced and documented datasets. Experiments show that SSL is beneficial for most but not all tasks which confirms the need for exhaustive and reliable benchmarks to evaluate its real impact. LeBenchmark is shared with the scientific community for reproducible research in SSL from speech.
Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; ASR; Automatic Emotion Recognition; Self-Supervised Representation Learning; SLU; Speech Translation
URL: https://hal.archives-ouvertes.fr/hal-03317730v2/file/FLOWBERT_IS2021.pdf
https://hal.archives-ouvertes.fr/hal-03317730v2/document
https://hal.archives-ouvertes.fr/hal-03317730
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5
Investigating Language Impact in Bilingual Approaches for Computational Language Documentation
In: Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), ; SLTU-CCURL workshop, LREC 2020 ; https://hal.archives-ouvertes.fr/hal-02895907 ; SLTU-CCURL workshop, LREC 2020, May 2020, Marseille, France (2020)
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6
MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible
In: Proceedings of The 12th Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02611059 ; Proceedings of The 12th Language Resources and Evaluation Conference, May 2020, Marseille, France. pp.6486 - 6493 (2020)
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7
Empirical Evaluation of Sequence-to-Sequence Models for Word Discovery in Low-resource Settings
In: Interspeech 2019 ; https://hal.archives-ouvertes.fr/hal-02193867 ; Interspeech 2019, Sep 2019, Graz, Austria (2019)
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8
How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages
In: Journées Scientifiques du Groupement de Recherche: Linguistique Informatique, Formelle et de Terrain (LIFT). ; https://hal.archives-ouvertes.fr/hal-02895895 ; Journées Scientifiques du Groupement de Recherche: Linguistique Informatique, Formelle et de Terrain (LIFT)., Nov 2019, Orléans, France (2019)
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9
A small Griko-Italian speech translation corpus
In: 6th international workshop on spoken language technologies for under-resourced languages(SLTU'18) ; https://hal.archives-ouvertes.fr/hal-01962528 ; 6th international workshop on spoken language technologies for under-resourced languages(SLTU'18), Aug 2018, New Delhi, India (2018)
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10
Unsupervised Word Segmentation from Speech with Attention
In: Interspeech 2018 ; https://hal.archives-ouvertes.fr/hal-01818092 ; Interspeech 2018, Sep 2018, Hyderabad, India (2018)
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11
Unsupervised Word Segmentation from Speech with Attention ...
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12
Unwritten Languages Demand Attention Too! Word Discovery with Encoder-Decoder Models
In: IEEE Automatic Speech Recognition and Understanding (ASRU) ; https://hal.archives-ouvertes.fr/hal-01592091 ; IEEE Automatic Speech Recognition and Understanding (ASRU), Dec 2017, Okinawa, Japan (2017)
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13
Unsupervised Word Discovery Using Attentional Encoder-Decoder Models
In: WiNLP workshop, ACL 2017 ; https://hal.archives-ouvertes.fr/hal-02895851 ; WiNLP workshop, ACL 2017, Jul 2017, Vancouver, Canada (2017)
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