DE eng

Search in the Catalogues and Directories

Page: 1 2 3 4 5...11
Hits 1 – 20 of 217

1
Using Automatic Speech Recognition to Optimize Hearing-Aid Time Constants
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
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 ...
BASE
Show details
5
Common Phone: A Multilingual Dataset for Robust Acoustic Modelling ...
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
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)
In: Sensors; Volume 22; Issue 4; Pages: 1583 (2022)
Abstract: The Montreal cognitive assessment (MoCA), a widely accepted screening tool for identifying patients with mild cognitive impairment (MCI), includes a language fluency test of verbal functioning; its scores are based on the number of unique correct words produced by the test taker. However, it is possible that unique words may be counted differently for various languages. This study focuses on Thai as a language that differs from English in terms of word combinations. We applied various automatic speech recognition (ASR) techniques to develop an assisted scoring system for the MoCA language fluency test with Thai language support. This was a challenge because Thai is a low-resource language for which domain-specific data are not publicly available, especially speech data from patients with MCIs. Furthermore, the great variety of pronunciation, intonation, tone, and accent of the patients, all of which might differ from healthy controls, bring more complexity to the model. We propose a hybrid time delay neural network hidden Markov model (TDNN-HMM) architecture for acoustic model training to create our ASR system that is robust to environmental noise and to the variation of voice quality impacted by MCI. The LOTUS Thai speech corpus was incorporated into the training set to improve the model’s generalization. A preprocessing algorithm was implemented to reduce the background noise and improve the overall data quality before feeding data into the TDNN-HMM system for automatic word detection and language fluency score calculation. The results show that the TDNN-HMM model in combination with data augmentation using lattice-free maximum mutual information (LF-MMI) objective function provides a word error rate (WER) of 30.77%. To our knowledge, this is the first study to develop an ASR with Thai language support to automate the scoring system of MoCA’s language fluency assessment.
Keyword: ASR; language fluency test; LOTUS corpus; MoCA; scoring; speech recognition; Thai tonal language
URL: https://doi.org/10.3390/s22041583
BASE
Hide details
9
Automatic Speech Recognition Performance Improvement for Mandarin Based on Optimizing Gain Control Strategy
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
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
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
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
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
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
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

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
217
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern