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Using Automatic Speech Recognition to Optimize Hearing-Aid Time Constants
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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)
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MAGIC DUST FOR CROSS-LINGUAL ADAPTATION OF MONOLINGUAL WAV2VEC-2.0
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In: ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03544515 ; ICASSP 2022, May 2022, Singapour, Singapore (2022)
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OGAHIYNING TARIXIY ASARLARIDAGI FONETIK O‘ZGARISHLAR XUSUSIDA ...
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Common Phone: A Multilingual Dataset for Robust Acoustic Modelling ...
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Common Phone: A Multilingual Dataset for Robust Acoustic Modelling ...
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Treasure Hunters 2: exploration of speech training efficacy ...
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Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System
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In: Sustainability; Volume 14; Issue 2; Pages: 614 (2022)
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Using Automatic Speech Recognition to Assess Thai Speech Language Fluency in the Montreal Cognitive Assessment (MoCA)
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In: Sensors; Volume 22; Issue 4; Pages: 1583 (2022)
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Automatic Speech Recognition Performance Improvement for Mandarin Based on Optimizing Gain Control Strategy
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In: Sensors; Volume 22; Issue 8; Pages: 3027 (2022)
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A Comparison of Hybrid and End-to-End ASR Systems for the IberSpeech-RTVE 2020 Speech-to-Text Transcription Challenge
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In: Applied Sciences; Volume 12; Issue 2; Pages: 903 (2022)
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Google Translate as a tool for self-directed language learning
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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
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| 12 |
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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| 14 |
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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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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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
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In: https://tel.archives-ouvertes.fr/tel-03616588 ; Intelligence artificielle [cs.AI]. Université Paul Sabatier - Toulouse III, 2021. Français. ⟨NNT : 2021TOU30116⟩ (2021)
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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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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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Abstract:
Automatic Speech Recognition (ASR) has made significant progress thanks to the advent of deep neural networks (DNNs). In the context of under-resourced languages, for which few resources are available, spectacular achievements has been reported. ASR systems are a step forward for language documentation, as the annotation cost is considerably reduced for field linguists (manually annotated an audio file can take a tremendous amount of time), and the language is preserved and perpetuated through documentation. Previous `standard' deep neural networks reached very good performances for phonemic transcription (such as with Kaldi and ESPnet approaches).However, these methods only rely on the phoneme-level. In this thesis, we explore recently published ASR approaches which have shown to be effective on low-resource languages to produce word-level audio-aligned transcriptions. The first approach, based on self-supervised learning, is a speech model that uses a Connectionist Temporal Classification (CTC). The second, entitled wav2vec-U, proposes a framework intended to build an ASR system in a fully unsupervised fashion. With few resources at our disposal, we try to assess the usability that can be made from dictionaries. We conducted experiments on two low-resource corpora, the Yongning Na and the Japhug from the Pangloss Collection. The experimental results from the first approach demonstrate powerful word-level transcriptions with competitive error rates. Preliminary results are reported on the second approach. By a coverage measure of dictionaries on the available transcriptions, we show that these resources are not yet usable in the conducted approaches.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; Automatic Speech Recognition ASR; deep learning; Machine learning; Neural networks
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URL: https://hal.archives-ouvertes.fr/hal-03429051/file/Macaire2021_RecognizingLexicalUnits.pdf
https://hal.archives-ouvertes.fr/hal-03429051
https://hal.archives-ouvertes.fr/hal-03429051/document
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Discriminative feature modeling for statistical speech recognition ...
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