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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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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)
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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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7
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)
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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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16
Investigating Techniques for Low Resource Conversational Speech Recognition
Abstract: In this paper we investigate various techniques in order to build effective speech to text (STT) and keyword search (KWS) systems for low resource conversational speech. Subword decoding and graphemic mappings were assessed in order to detect out-of-vocabulary keywords. To deal with the limited amount of transcribed data, semi-supervised training and data selection methods were investigated. Robust acoustic features produced via data augmentation were evaluated for acoustic modeling. For language modeling, automatically retrieved conversational-like Webdata was used, as well as neural network based models. We report STT improvements with all the techniques, but interestingly only some improve KWS performance. Results are reported for the Swahili language in the context of the 2015 OpenKWS Evaluation. ; 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ISASSP) , 20 Mar 2016, 25 Mar 2016
Keyword: acoustics; conversational speech; data models; feature extraction; Hidden Markov models; IARPA Collection; keyword search; keyword spotting; low-resource languages; speech; speech recognition; training
URL: http://www.dtic.mil/docs/citations/AD1038530
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=AD1038530
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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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