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Investigating alignment interpretability for low-resource NMT
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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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Impact of Encoding and Segmentation Strategies on End-to-End Simultaneous Speech Translation
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In: INTERSPEECH 2021 ; https://hal.archives-ouvertes.fr/hal-03372487 ; INTERSPEECH 2021, Aug 2021, Brno, Czech Republic (2021)
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Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input
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In: Interspeech 2021 - 22nd Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03372802 ; Interspeech 2021 - 22nd Annual Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic. pp.3865-3869, ⟨10.21437/Interspeech.2021-275⟩ (2021)
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LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
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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
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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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LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
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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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| 8 |
Contribution d'informations syntaxiques aux capacités de généralisation compositionelle des modèles seq2seq convolutifs
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In: Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale ; Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-03265890 ; Traitement Automatique des Langues Naturelles, 2021, Lille, France. pp.134-141 (2021)
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Lightweight Adapter Tuning for Multilingual Speech Translation
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In: The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) ; https://hal.archives-ouvertes.fr/hal-03294912 ; The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), Aug 2021, Bangkok (Virtual), Thailand (2021)
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Visualizing Cross-Lingual Discourse Relations in Multilingual TED Corpora
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In: Proceedings of the 2nd Workshop on Computational Approaches to Discourse ; CODI 2021: 2nd Workshop on Computational Approaches to Discourse ; https://hal.archives-ouvertes.fr/hal-03642341 ; CODI 2021: 2nd Workshop on Computational Approaches to Discourse, Nov 2021, Punta Cana, Dominican Republic. ⟨10.18653/v1/2021.codi-main.16⟩ (2021)
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Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads?
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In: Findings of ACL 2021 ; https://hal.archives-ouvertes.fr/hal-03299010 ; Findings of ACL 2021, Aug 2021, Bangkok (virtual), Thailand (2021)
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User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis
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In: ComputEL-4: Fourth Workshop on the Use of Computational Methods in the Study of Endangered Languages ; https://halshs.archives-ouvertes.fr/halshs-03030529 ; ComputEL-4: Fourth Workshop on the Use of Computational Methods in the Study of Endangered Languages, Mar 2021, Hawai‘i, United States (2021)
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Segmentation en mots faiblement supervisée pour la documentation automatique des langues
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In: https://hal.archives-ouvertes.fr/hal-03477475 ; 2021 (2021)
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Investigating the Impact of Gender Representation in ASR Training Data: a Case Study on Librispeech
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In: Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing ; 3rd Workshop on Gender Bias in Natural Language Processing ; https://hal.univ-grenoble-alpes.fr/hal-03472117 ; 3rd Workshop on Gender Bias in Natural Language Processing, Aug 2021, Online, France. pp.86-92, ⟨10.18653/v1/2021.gebnlp-1.10⟩ (2021)
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Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads? ...
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Lightweight Adapter Tuning for Multilingual Speech Translation ...
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Multilingual Unsupervised Neural Machine Translation with Denoising Adapters ...
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| 18 |
Controlling Prosody in End-to-End TTS: A Case Study on Contrastive Focus Generation ...
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| 19 |
Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings ...
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Abstract:
When documenting oral-languages, Unsupervised Word Segmentation (UWS) from speech is a useful, yet challenging, task. It can be performed from phonetic transcriptions, or in the absence of these, from the output of unsupervised speech discretization models. These discretization models are trained using raw speech only, producing discrete speech units which can be applied for downstream (text-based) tasks. In this paper we compare five of these models: three Bayesian and two neural approaches, with regards to the exploitability of the produced units for UWS. Two UWS models are experimented with and we report results for Finnish, Hungarian, Mboshi, Romanian and Russian in a low-resource setting (using only 5k sentences). Our results suggest that neural models for speech discretization are difficult to exploit in our setting, and that it might be necessary to adapt them to limit sequence length. We obtain our best UWS results by using the SHMM and H-SHMM Bayesian models, which produce high quality, yet ...
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Keyword:
Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
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URL: https://dx.doi.org/10.48550/arxiv.2106.04298
https://arxiv.org/abs/2106.04298
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Lightweight Adapter Tuning for Multilingual Speech Translation ...
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