| 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 |
RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION
|
|
|
|
In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Fine-tuning pre-trained models for Automatic Speech Recognition: experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)
|
|
|
|
In: https://halshs.archives-ouvertes.fr/halshs-03647315 ; 2022 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Treasure Hunters 2: exploration of speech training efficacy ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
MLLP-VRAIN Spanish ASR Systems for the Albayzín-RTVE 2020 Speech-to-Text Challenge: Extension
|
|
|
|
In: Applied Sciences; Volume 12; Issue 2; Pages: 804 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database
|
|
|
|
In: Applied Sciences; Volume 12; Issue 4; Pages: 1889 (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 |
Automatic Speech Recognition (ASR) Systems for Children: A Systematic Literature Review
|
|
|
|
In: Applied Sciences; Volume 12; Issue 9; Pages: 4419 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 11 |
An Empirical Performance Analysis of the Speak Correct Computerized Interface
|
|
|
|
In: Processes; Volume 10; Issue 3; Pages: 487 (2022)
|
|
Abstract:
The way in which people speak reveals a lot about where they are from, where they were raised, and also where they have recently lived. When communicating in a foreign language or second language, accents from one’s first language are likely to emerge, giving an individual a ‘strange’ accent. This is a great and challenging problem. Not particularly, because it is a part of one’s personality that they do not have to give up. It is only challenging when pronunciation causes a disruption in communication between an individual and the individuals with whom they are speaking. Making oneself understandable is the goal of perfecting English pronunciations. Many people require their pronunciation to be perfect, such as those individuals working in the healthcare industry, where it is rather critical that each term be read precisely. Speak Correct offers each of its users a service that assists them with any English pronunciation concerns that may arise. Some of the pronunciation improvements will only apply to a specific customer’s dictionary; however, in some cases, the modifications can be applied to the standard dictionary as well, benefiting our whole customer base. Speak Correct is a computerized linguist interface that can assist its users in many different places around the world with their English pronunciation issues due to Saudi or Egyptian accents. In this study, the authors carry out an empirical investigation of the Speak Correct computerized interface to assess its performance. The results of this research reveal that Speak Correct is highly effective at delivering pronunciation correction.
|
|
Keyword:
automatic speech recognition; computerized interface; empirical assessment; pronunciation correction; speech processing
|
|
URL: https://doi.org/10.3390/pr10030487
|
|
BASE
|
|
Hide details
|
|
|
| 12 |
Influence of Highly Inflected Word Forms and Acoustic Background on the Robustness of Automatic Speech Recognition for Human–Computer Interaction
|
|
|
|
In: Mathematics; Volume 10; Issue 5; Pages: 711 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Fine-tuning pre-trained models for Automatic Speech Recognition: experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)
|
|
|
|
In: https://halshs.archives-ouvertes.fr/halshs-03647315 ; 2022 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Automatic Speech Recognition systems errors for accident-prone sleepiness detection through voice
|
|
|
|
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
|
|
|
| 16 |
Automatic Speech Recognition systems errors for objective sleepiness detection through voice
|
|
|
|
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
|
|
|
| 17 |
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
|
|
|
| 18 |
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
|
|
|
| 19 |
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
|
|
|
| 20 |
Re-synchronization using the Hand Preceding Model for Multi-modal Fusion in Automatic Continuous Cued Speech Recognition
|
|
|
|
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
|
|
|
|
|
