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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 5...11 Hits 1 – 20 of 217 1 Using Automatic Speech Recognition to Optimize Hearing-Aid Time Constants Fontan, Lionel; Gonçalves Braz, Libio; Pinquier, Julien... In: ISSN: 1662-4548 ; EISSN: 1662-453X ; Frontiers in Neuroscience ; https://hal.archives-ouvertes.fr/hal-03627441 ; Frontiers in Neuroscience, Frontiers, 2022, 16 (779062), ⟨10.3389/fnins.2022.779062⟩ ; https://www.frontiersin.org/articles/10.3389/fnins.2022.779062/full (2022) BASE Show details 2 MAGIC DUST FOR CROSS-LINGUAL ADAPTATION OF MONOLINGUAL WAV2VEC-2.0 Khurana, Sameer; Laurent, Antoine; Glass, James In: ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03544515 ; ICASSP 2022, May 2022, Singapour, Singapore (2022) BASE Show details 3 OGAHIYNING TARIXIY ASARLARIDAGI FONETIK O‘ZGARISHLAR XUSUSIDA ... Qunduzoy Xajiboyeva. - : Academic research in educational sciences, 2022 BASE Show details 4 Common Phone: A Multilingual Dataset for Robust Acoustic Modelling ... Klumpp, Philipp; Arias-Vergara, Tomás; Pérez-Toro, Paula Andrea; Nöth, Elmar; Orozco-Arroyave, Juan Rafael. - : Zenodo, 2022 Abstract: Release Date: 17.01.22 Welcome to Common Phone 1.0 Legal Information Common Phone is a subset of the Common Voice corpus collected by Mozilla Corporation . By using Common Phone , you agree to the Common Voice Legal Terms. Common Phone is maintained and distributed by speech researchers at the Pattern Recognition Lab of Friedrich-Alexander-University Erlangen-Nuremberg (FAU) under the CC0 license. Like for Common Voice , you must not make any attempt to identify speakers that contributed to Common Phone . About Common Phone This corpus aims to provide a basis for Machine Learning (ML) researchers and enthusiasts to train and test their models against a wide variety of speakers, hardware/software ecosystems and acoustic conditions to improve generalization and availability of ML in real-world speech applications. The current version of Common Phone comprises 116,5 hours of speech samples, collected from 11.246 speakers in 6 languages: Language Speakers Hours train / dev / test train / dev / test English 4716 ... : {"references": ["Klumpp, Philipp et al. (2022); \"Common Phone: A Multilingual Dataset for Robust Acoustic Modelling\" https://arxiv.org/abs/2201.05912"]} ... Keyword: ASR; Machine Learning; Multilingual; Phoneme Recognition; Phonetic Annotation; Speech; Speech Processing URL: https://zenodo.org/record/5846137 https://dx.doi.org/10.5281/zenodo.5846137 BASE Hide details 5 Common Phone: A Multilingual Dataset for Robust Acoustic Modelling ... Klumpp, Philipp; Arias-Vergara, Tomás; Pérez-Toro, Paula Andrea. - : Zenodo, 2022 BASE Show details 6 Treasure Hunters 2: exploration of speech training efficacy ... Ganzeboom, Mario; Bakker, Marjoke; Beijer, Lilian. - : Radboud University, 2022 BASE Show details 7 Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System Taniya Hasija; Virender Kadyan; Kalpna Guleria; Abdullah Alharbi; Hashem Alyami; Nitin Goyal In: Sustainability; Volume 14; Issue 2; Pages: 614 (2022) BASE Show details 8 Using Automatic Speech Recognition to Assess Thai Speech Language Fluency in the Montreal Cognitive Assessment (MoCA) Pimarn Kantithammakorn; Proadpran Punyabukkana; Ploy N. Pratanwanich; Solaphat Hemrungrojn; Chaipat Chunharas; Dittaya Wanvarie In: Sensors; Volume 22; Issue 4; Pages: 1583 (2022) BASE Show details 9 Automatic Speech Recognition Performance Improvement for Mandarin Based on Optimizing Gain Control Strategy Desheng Wang; Yangjie Wei; Ke Zhang; Dong Ji; Yi Wang In: Sensors; Volume 22; Issue 8; Pages: 3027 (2022) BASE Show details 10 A Comparison of Hybrid and End-to-End ASR Systems for the IberSpeech-RTVE 2020 Speech-to-Text Transcription Challenge Juan M. Perero-Codosero; Fernando M. Espinoza-Cuadros; Luis A. Hernández-Gómez In: Applied Sciences; Volume 12; Issue 2; Pages: 903 (2022) BASE Show details 11 Google Translate as a tool for self-directed language learning van Lieshout, Catharina; Cardoso, Walcir. - : University of Hawaii National Foreign Language Resource Center, 2022. : Center for Language & Technology, 2022. : (co-sponsored by Center for Open Educational Resources and Language Learning, University of Texas at Austin), 2022 BASE Show details 12 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 13 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 14 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 15 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 16 Automatic Speech Recognition : from hybrid to end-to-end approach ; Reconnaissance automatique de la parole à large vocabulaire : des approches hybrides aux approches End-to-End Heba, Abdelwahab. - : HAL CCSD, 2021 In: https://tel.archives-ouvertes.fr/tel-03616588 ; Intelligence artificielle [cs.AI]. Université Paul Sabatier - Toulouse III, 2021. Français. ⟨NNT : 2021TOU30116⟩ (2021) BASE Show details 17 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 18 ESIC 1.0 -- Europarl Simultaneous Interpreting Corpus Macháček, Dominik; Žilinec, Matúš; Bojar, Ondřej. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2021 BASE Show details 19 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 20 Discriminative feature modeling for statistical speech recognition ... Tüske, Zoltán. - : RWTH Aachen University, 2021 BASE Show details Page: 1 2 3 4 5...11

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