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 ...
|
|
|
|
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 ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System
|
|
|
|
In: Sustainability; Volume 14; Issue 2; Pages: 614 (2022)
|
|
Abstract:
Speech recognition has been an active field of research in the last few decades since it facilitates better human–computer interaction. Native language automatic speech recognition (ASR) systems are still underdeveloped. Punjabi ASR systems are in their infancy stage because most research has been conducted only on adult speech systems; however, less work has been performed on Punjabi children’s ASR systems. This research aimed to build a prosodic feature-based automatic children speech recognition system using discriminative modeling techniques. The corpus of Punjabi children’s speech has various runtime challenges, such as acoustic variations with varying speakers’ ages. Efforts were made to implement out-domain data augmentation to overcome such issues using Tacotron-based text to a speech synthesizer. The prosodic features were extracted from Punjabi children’s speech corpus, then particular prosodic features were coupled with Mel Frequency Cepstral Coefficient (MFCC) features before being submitted to an ASR framework. The system modeling process investigated various approaches, which included Maximum Mutual Information (MMI), Boosted Maximum Mutual Information (bMMI), and feature-based Maximum Mutual Information (fMMI). The out-domain data augmentation was performed to enhance the corpus. After that, prosodic features were also extracted from the extended corpus, and experiments were conducted on both individual and integrated prosodic-based acoustic features. It was observed that the fMMI technique exhibited 20% to 25% relative improvement in word error rate compared with MMI and bMMI techniques. Further, it was enhanced using an augmented dataset and hybrid front-end features (MFCC + POV + Fo + Voice quality) with a relative improvement of 13% compared with the earlier baseline system.
|
|
Keyword:
children Punjabi ASR; data augmentation; discriminative techniques; feature extraction; prosodic features
|
|
URL: https://doi.org/10.3390/su14020614
|
|
BASE
|
|
Hide 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)
|
|
BASE
|
|
Show 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
|
|
|
|
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
|
|
|
|
In: https://hal.archives-ouvertes.fr/tel-03269807 ; Son [cs.SD]. Université toulouse 3 Paul Sabatier, 2021. Français (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 ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|