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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 Hits 1 – 20 of 61 1 Automatic Speech Recognition systems errors for accident-prone sleepiness detection through voice Martin, Vincent,; Rouas, Jean-Luc; Boyer, Florian... In: EUSIPCO 2021 ; https://hal.archives-ouvertes.fr/hal-03324033 ; EUSIPCO 2021, Aug 2021, Dublin (en ligne), Ireland. ⟨10.23919/EUSIPCO54536.2021.9616299⟩ (2021) BASE Show details 2 Automatic Speech Recognition systems errors for objective sleepiness detection through voice Martin, Vincent,; Rouas, Jean-Luc; Boyer, Florian... 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) BASE Show details 3 LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech Evain, Solène; Nguyen, Ha; Le, Hang... 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) BASE Show details 4 LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech Evain, Solène; Nguyen, Ha; Le, Hang... 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) BASE Show details 5 LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech Evain, Solène; Nguyen, Ha; Le, Hang... 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) BASE Show details 6 Re-synchronization using the Hand Preceding Model for Multi-modal Fusion in Automatic Continuous Cued Speech Recognition Liu, Li; Feng, Gang; Beautemps, Denis... 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) BASE Show details 7 Identifying Speaker State from Multimodal Cues Yang, Zixiaofan. - 2021 BASE Show details 8 Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios Eskander, Ramy. - 2021 BASE Show details 9 Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks MACAIRE, Cécile In: https://hal.archives-ouvertes.fr/hal-03429051 ; [Research Report] LACITO (UMR 7107). 2021 (2021) BASE Show details 10 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 Abstract: Recently, adult automatic speech recognition (ASR) system performance has improved dramatically. In contrast, the performance of child ASR systems remains inadequate in an era where demand for child speech technology is on the rise. While adult speech data is abundant, publicly available child speech data is sparse due, in part, to privacy concerns. Hence, many child ASR systems are trained using adult speech data. However, child ASR systems perform poorly when trained on adult speech due to the acoustic mismatch that results from body size differences, especially the vocal folds and the vocal tract, as well as the high variability of child speech.This research analyzes the acoustical properties of child speech across various ages and compares them to the acoustic properties of adult speech. Specifically, the subglottal resonances (SGRs), fundamental frequency (fo), and formant frequencies of vowel productions are investigated. These acoustic features are shown to be capable of predicting acoustic structures across speakers. As such, we propose feature extraction methods utilizing these properties to normalize the acoustic structure across speakers and reduce the acoustic mismatch between adult and child speech. This allows child ASR systems to leverage adult data for training and suggests a framework for a universal ASR system that need not be adult or child dependent. Furthermore, we demonstrate that when child speech data is limited, these feature normalization methods are capable of producing significant improvements in child ASR for both Gaussian mixture model (GMM) and deep neural network (DNN)-based systems. Keyword: automatic speech recognition; child speech; Electrical engineering; fundamental frequency; subglottal resonances URL: https://escholarship.org/uc/item/3fs24080 BASE Hide details 11 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) BASE Show details 12 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 13 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 14 The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021) BASE Show details 15 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 16 The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021) BASE Show details 17 Enhancing Speech Privacy with Slicing Maouche, Mohamed; Srivastava, Brij Mohan Lal; Vauquier, Nathalie... In: https://hal.inria.fr/hal-03369137 ; 2021 (2021) BASE Show details 18 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 19 Training RNN Language Models on Uncertain ASR Hypotheses in Limited Data Scenarios Sheikh, Imran,; Vincent, Emmanuel; Illina, Irina In: https://hal.inria.fr/hal-03327306 ; 2021 (2021) BASE Show details 20 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details Page: 1 2 3 4

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