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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)
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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)
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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)
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Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0 ...
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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)
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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)
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CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning ...
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8
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning ...
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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)
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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)
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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)
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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)
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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
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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)
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15
Improving Data Selection for Low Resource STT and KWS
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Investigating Techniques for Low Resource Conversational Speech Recognition
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17
Improving recognition of proper nouns in ASR through generating and filtering phonetic transcriptions
In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 28 (2014) 4, 979-996
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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)
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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)
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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)
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