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1
Applying phonetics : speech science in everyday life
Munro, Murray J.. - Chichester, West Sussex : Wiley Blackwell, 2021
BLLDB
UB Frankfurt Linguistik
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2
Automatic Speech Recognition systems errors for accident-prone sleepiness detection through voice
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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3
Automatic Speech Recognition systems errors for objective sleepiness detection through voice
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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4
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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5
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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6
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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7
Re-synchronization using the Hand Preceding Model for Multi-modal Fusion in Automatic Continuous Cued Speech Recognition
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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8
Identifying Speaker State from Multimodal Cues
Yang, Zixiaofan. - 2021
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9
Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios
Eskander, Ramy. - 2021
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10
Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks
In: https://hal.archives-ouvertes.fr/hal-03429051 ; [Research Report] LACITO (UMR 7107). 2021 (2021)
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11
Speech Normalization and Data Augmentation Techniques Based on Acoustical and Physiological Constraints and Their Applications to Child Speech Recognition
Yeung, Gary Joseph. - : eScholarship, University of California, 2021
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12
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
Heba, Abdelwahab. - : HAL CCSD, 2021
In: https://hal.archives-ouvertes.fr/tel-03269807 ; Son [cs.SD]. Université toulouse 3 Paul Sabatier, 2021. Français (2021)
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13
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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14
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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.
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
URL: https://hal.archives-ouvertes.fr/hal-03335126v4/document
https://hal.archives-ouvertes.fr/hal-03335126v4/file/Supplementary_hal.pdf
https://hal.archives-ouvertes.fr/hal-03335126
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15
The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
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16
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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17
The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
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18
Enhancing Speech Privacy with Slicing
In: https://hal.inria.fr/hal-03369137 ; 2021 (2021)
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19
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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20
Training RNN Language Models on Uncertain ASR Hypotheses in Limited Data Scenarios
In: https://hal.inria.fr/hal-03327306 ; 2021 (2021)
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