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RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION
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In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022)
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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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Where are we in semantic concept extraction for Spoken Language Understanding? ⋆
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In: SPECOM 2021 23rd International Conference on Speech and Computer ; https://hal.archives-ouvertes.fr/hal-03372494 ; SPECOM 2021 23rd International Conference on Speech and Computer, Sep 2021, Saint Petersburg, Russia (2021)
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Abstract:
International audience ; Spoken language understanding (SLU) topic has seen a lot of progress these last three years, with the emergence of end-to-end neural approaches. Spoken language understanding refers to natural language processing tasks related to semantic extraction from speech signal, like named entity recognition from speech or slot filling task in a context of human-machine dialogue. Classically, SLU tasks were processed through a cascade approach that consists in applying, firstly, an automatic speech recognition process, followed by a natural language processing module applied to the automatic transcriptions. These three last years, end-toend neural approaches, based on deep neural networks, have been proposed in order to directly extract the semantics from speech signal, by using a single neural model. More recent works on self-supervised training with unlabeled data open new perspectives in term of performance for automatic speech recognition and natural language processing. In this paper, we present a brief overview of the recent advances on the French MEDIA benchmark dataset for SLU, with or without the use of additional data. We also present our last results that significantly outperform the current state-of-the-art with a Concept Error Rate (CER) of 11.2%, instead of 13.6% for the last state-of-the-art system presented this year.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; Cascade approach; End-to-end approach; Self supervised training; Spoken language understanding
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URL: https://hal.archives-ouvertes.fr/hal-03372494/file/2106.13045.pdf https://hal.archives-ouvertes.fr/hal-03372494/document https://hal.archives-ouvertes.fr/hal-03372494
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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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ON-TRAC' systems for the IWSLT 2021 low-resource speech translation and multilingual speech translation shared tasks
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In: Proceedings of the 18th International Conference on Spoken Language Translation, ; International Conference on Spoken Language Translation (IWSLT) ; https://hal.archives-ouvertes.fr/hal-03298854 ; International Conference on Spoken Language Translation (IWSLT), Aug 2021, Bangkok (virtual), Thailand. ⟨10.18653/v1/2021.iwslt-1.20⟩ (2021)
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On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition
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In: IEEE Spoken Language Technology Workshop ; https://hal.archives-ouvertes.fr/hal-03003469 ; IEEE Spoken Language Technology Workshop, Jan 2021, Virtual, China (2021)
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Where are we in Named Entity Recognition from Speech?
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In: 12th International Conference on Language Resources and Evaluation (LREC) ; https://hal.archives-ouvertes.fr/hal-02475026 ; 12th International Conference on Language Resources and Evaluation (LREC), May 2020, Marseille, France ; https://aclanthology.org/2020.lrec-1.556/ (2020)
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ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020
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In: Proceedings of the 17th International Conference on Spoken Language Translation ; https://hal.archives-ouvertes.fr/hal-02895893 ; Proceedings of the 17th International Conference on Spoken Language Translation, Jul 2020, Seattle, WA, United States. pp.35-43, ⟨10.18653/v1/2020.iwslt-1.2⟩ (2020)
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On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition ...
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Error detection of grapheme-to-phoneme conversion in text-to-speech synthesis using speech signal and lexical context
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In: 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) ; https://hal.archives-ouvertes.fr/hal-01585770 ; 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Dec 2017, Okinawa, Japan. ⟨10.1109/ASRU.2017.8269004⟩ ; https://asru2017.org (2017)
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LIUM ASR systems for the 2016 Multi-Genre Broadcast Arabic Challenge
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In: IEEE Workshop on Spoken Language Technology ; https://hal.archives-ouvertes.fr/hal-01433188 ; IEEE Workshop on Spoken Language Technology, Dec 2016, San Diego, CA, USA, United States. ⟨10.1109/SLT.2016.7846278⟩ ; http://www.slt2016.org (2016)
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Enhancing the RATP-DECODA corpus with linguistic annotations for performing a large range of NLP tasks
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In: 10th edition of the Language Resources and Evaluation Conference (LREC 2016) ; https://hal.archives-ouvertes.fr/hal-01433189 ; 10th edition of the Language Resources and Evaluation Conference (LREC 2016), 2016, Portorož, Slovenia (2016)
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Phonétisation automatique du dialecte tunisien
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In: JEP 2014 ; https://hal.archives-ouvertes.fr/hal-01433231 ; JEP 2014, 2014, Le Mans, France (2014)
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Reconnaissance automatique de la parole
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In: ISSN: 0222-9838 ; EISSN: 1783-1601 ; L'information grammaticale ; https://hal.archives-ouvertes.fr/hal-01135037 ; L'information grammaticale, Peeters Publishers, 2014, TRAITEMENTS AUTOMATIQUES DE L’ORAL ET DE L’ÉCRIT (1) Panorama des recherches et des technologies actuelles, 141, pp.10 (2014)
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Phonetic tool for the Tunisian Arabic
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In: SLTU'2014, The 4th International Workshop on spoken Language Technologies for Under-resourced Languages ; https://hal.archives-ouvertes.fr/hal-01433236 ; SLTU'2014, The 4th International Workshop on spoken Language Technologies for Under-resourced Languages, 2014, Saint-Petersburg, Russia (2014)
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A Corpus and Phonetic Dictionary for Tunisian Arabic Speech Recognition
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In: The 9th edition of the Language Resources and Evaluation Conference (LREC 2014) ; https://hal.archives-ouvertes.fr/hal-01433247 ; The 9th edition of the Language Resources and Evaluation Conference (LREC 2014), 2014, Reykjavik, Iceland (2014)
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