1 |
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
|
|
2 |
End-to-end speaker segmentation for overlap-aware resegmentation
|
|
|
|
In: Interspeech 2021 ; https://hal-univ-lemans.archives-ouvertes.fr/hal-03257524 ; Interspeech 2021, Aug 2021, Brno, Czech Republic ; https://www.interspeech2021.org/ (2021)
|
|
BASE
|
|
Show details
|
|
3 |
Transdisciplinary Analysis of a Corpus of French Newsreels: The ANTRACT Project
|
|
|
|
In: ISSN: 1938-4122 ; Digital Humanities Quarterly ; https://hal.archives-ouvertes.fr/hal-03166755 ; Digital Humanities Quarterly, Alliance of Digital Humanities, 2021, Special Issue on AudioVisual Data in DH, 15 (1) ; http://digitalhumanities.org/dhq/ (2021)
|
|
BASE
|
|
Show details
|
|
4 |
Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0 ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Where are we in Named Entity Recognition from Speech?
|
|
|
|
In: 12th International Conference on Language Resources and Evaluation (LREC) ; https://hal.archives-ouvertes.fr/hal-02475026 ; 12th International Conference on Language Resources and Evaluation (LREC), May 2020, Marseille, France ; https://aclanthology.org/2020.lrec-1.556/ (2020)
|
|
BASE
|
|
Show details
|
|
6 |
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning
|
|
|
|
In: Interspeech 2020 ; https://hal.archives-ouvertes.fr/hal-02912029 ; Interspeech 2020, Oct 2020, Shanghai, China (2020)
|
|
BASE
|
|
Show details
|
|
7 |
CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Collective memory shapes the organization of individual memories in the medial prefrontal cortex
|
|
|
|
In: EISSN: 2397-3374 ; Nature Human Behaviour ; https://halshs.archives-ouvertes.fr/halshs-02416130 ; Nature Human Behaviour, Nature Research 2019, ⟨10.1038/s41562-019-0779-z⟩ (2019)
|
|
BASE
|
|
Show details
|
|
10 |
Effective keyword search for low-resourced conversational speech
|
|
|
|
In: icassp 2017 ; https://hal.archives-ouvertes.fr/hal-01744176 ; icassp 2017, IEEE, Mar 2017, La Nouvelle Orléans, United States (2017)
|
|
BASE
|
|
Show details
|
|
11 |
An investigation into language model data augmentation for low-resourced STT and KWS
|
|
|
|
In: IEEE International Conference on Acoustics, Speech, and Signal Processing ; https://hal.archives-ouvertes.fr/hal-01837171 ; IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, Mar 2017, New Orleans, United States (2017)
|
|
BASE
|
|
Show details
|
|
12 |
Language Recognition for Dialects and Closely Related Languages
|
|
|
|
In: Odyssey 2016 ; https://hal.archives-ouvertes.fr/hal-01744188 ; Odyssey 2016, Jun 2016, Bilbao, Spain (2016)
|
|
BASE
|
|
Show details
|
|
13 |
Language Model Data Augmentation for Keyword Spotting
|
|
|
|
In: Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-01837186 ; Annual Conference of the International Speech Communication Association , Jan 2016, San Francisco, United States (2016)
|
|
Abstract:
International audience ; This research extends our earlier work on using machinetranslation (MT) and word-based recurrent neural networks toaugment language model training data for keyword search inconversational Cantonese speech. MT-based data augmenta-tion is applied to two language pairs: English-Lithuanian andEnglish-Amharic. Using filtered N-best MT hypotheses for lan-guage modeling is found to perform better than just using the 1-best translation. Target language texts collected from the Weband filtered to select conversational-like data are used in severalmanners. In addition to using Web data for training the languagemodel of the speech recognizer, we further investigate using thisdata to improve the language model and phrase table of the MTsystem to get better translations of the English data. Finally,generating text data with a character-based recurrent neural net-work is investigated. This approach allows new word forms tobe produced, providing a way to reduce the out-of-vocabularyrate and thereby improve keyword spotting performance. Westudy how these different methods of language model data aug-mentation impact speech-to-text and keyword spotting perfor-mance for the Lithuanian and Amharic languages. The best re-sults are obtained by combining all of the explored methods.
|
|
Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs]; language modeling; low-resourced languages; machine translation; speech recognition; text augmentation
|
|
URL: https://hal.archives-ouvertes.fr/hal-01837186
|
|
BASE
|
|
Hide details
|
|
14 |
Investigating techniques for low resource conversational speech recognition
|
|
|
|
In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016) ; https://hal-univ-lemans.archives-ouvertes.fr/hal-01515254 ; 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), Mar 2016, Shangai, China. pp.5975-5979, ⟨10.1109/ICASSP.2016.7472824⟩ ; www.icassp2016.org (2016)
|
|
BASE
|
|
Show details
|
|
16 |
Investigating Techniques for Low Resource Conversational Speech Recognition
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Traduction de la parole dans le projet RAPMAT
|
|
|
|
In: Journées d'Études sur la Parole ; https://hal.archives-ouvertes.fr/hal-01843418 ; Journées d'Études sur la Parole, Jan 2014, Le Mans, France (2014)
|
|
BASE
|
|
Show details
|
|
19 |
Boosting bonsai trees for efficient features combination : application to speaker role identification
|
|
|
|
In: Interspeech ; https://hal.inria.fr/hal-01025171 ; Interspeech, Sep 2014, Singapour, Singapore (2014)
|
|
BASE
|
|
Show details
|
|
20 |
Development of a Korean speech recognition system with little annontated data
|
|
|
|
In: International Workshop on Spoken Languages Technologies for Under-resourced languages ; https://hal.archives-ouvertes.fr/hal-01843405 ; International Workshop on Spoken Languages Technologies for Under-resourced languages, May 2014, St Petersburg, Russia (2014)
|
|
BASE
|
|
Show details
|
|
|
|