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Automatic Speech Recognition systems errors for accident-prone sleepiness detection through voice
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In: EUSIPCO 2021 ; https://hal.archives-ouvertes.fr/hal-03324033 ; EUSIPCO 2021, Aug 2021, Dublin (en ligne), Ireland. ⟨10.23919/EUSIPCO54536.2021.9616299⟩ (2021)
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Automatic Speech Recognition systems errors for objective sleepiness detection through voice
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In: Proceedings Interspeech 2021 ; Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03328827 ; Interspeech 2021, Aug 2021, Brno (virtual), Czech Republic. pp.2476-2480, ⟨10.21437/Interspeech.2021-291⟩ (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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Re-synchronization using the Hand Preceding Model for Multi-modal Fusion in Automatic Continuous Cued Speech Recognition
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In: ISSN: 1520-9210 ; IEEE Transactions on Multimedia ; https://hal.archives-ouvertes.fr/hal-02433830 ; IEEE Transactions on Multimedia, Institute of Electrical and Electronics Engineers, 2021, 23, pp.292-305. ⟨10.1109/TMM.2020.2976493⟩ (2021)
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Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios
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Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks
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In: https://hal.archives-ouvertes.fr/hal-03429051 ; [Research Report] LACITO (UMR 7107). 2021 (2021)
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Speech Normalization and Data Augmentation Techniques Based on Acoustical and Physiological Constraints and Their Applications to Child Speech Recognition
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Large vocabulary automatic speech recognition: from hybrid to end-to-end approaches ; Reconnaissance automatique de la parole à large vocabulaire : des approches hybrides aux approches End-to-End
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In: https://hal.archives-ouvertes.fr/tel-03269807 ; Son [cs.SD]. Université toulouse 3 Paul Sabatier, 2021. Français (2021)
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Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
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Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
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Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel; Patino, Jose; Srivastava, Brij Mohan Lal; Noé, Paul-Gauthier; Nautsch, Andreas; Evans, Nicholas; Yamagishi, Junichi; O'brien, Benjamin; Chanclu, Anaïs; Bonastre, Jean-François; Todisco, Massimiliano; Maouche, Mohamed
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In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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Abstract:
Supplementary material to the paper "The VoicePrivacy 2020 Challenge: Results and findings" (https://hal.archives-ouvertes.fr/hal-03332224) submitted to CSL. ; The VoicePrivacy 2020 Challenge focuses on developing anonymization solutions for speech technology. This report complements the summary results and analyses presented by Tomashenko et al. (2021). After quickly recalling the challenge design and the submitted anonymization systems, we provide more detailed results and analyses. First, we present objective evaluation results for the primary challenge metrics and for alternative metrics and attack models, and we compare them with each other. Second, we present subjective evaluation results for speaker verifiability, speech naturalness, and speech intelligibility. Finally, we compare these objective and subjective evaluation results with each other.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; anonymization; attack model; automatic speech recognition; metrics; privacy; speaker verification; speech synthesis; utility; voice conversion
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URL: https://hal.archives-ouvertes.fr/hal-03335126 https://hal.archives-ouvertes.fr/hal-03335126v1/document https://hal.archives-ouvertes.fr/hal-03335126v1/file/VoicePrivacyCSL_paper___report___for__hal.pdf
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The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
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Enhancing Speech Privacy with Slicing
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In: https://hal.inria.fr/hal-03369137 ; 2021 (2021)
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Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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Training RNN Language Models on Uncertain ASR Hypotheses in Limited Data Scenarios
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In: https://hal.inria.fr/hal-03327306 ; 2021 (2021)
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