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Collective memory shapes the organization of individual memories in the medial prefrontal cortex
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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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Effective keyword search for low-resourced conversational speech
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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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An investigation into language model data augmentation for low-resourced STT and KWS
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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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Language Recognition for Dialects and Closely Related Languages
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In: Odyssey 2016 ; https://hal.archives-ouvertes.fr/hal-01744188 ; Odyssey 2016, Jun 2016, Bilbao, Spain (2016)
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Language Model Data Augmentation for Keyword Spotting
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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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Investigating techniques for low resource conversational speech recognition
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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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Abstract:
International audience ; 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. Sub-word 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.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; conversational speech; keyword spotting; low-ressource languages; Speech recognition
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URL: https://hal-univ-lemans.archives-ouvertes.fr/hal-01515254/file/Laurent16.pdf
https://hal-univ-lemans.archives-ouvertes.fr/hal-01515254/document
https://doi.org/10.1109/ICASSP.2016.7472824
https://hal-univ-lemans.archives-ouvertes.fr/hal-01515254
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Traduction de la parole dans le projet RAPMAT
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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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Boosting bonsai trees for efficient features combination : application to speaker role identification
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In: Interspeech ; https://hal.inria.fr/hal-01025171 ; Interspeech, Sep 2014, Singapour, Singapore (2014)
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Development of a Korean speech recognition system with little annontated data
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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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Développement d'un système de reconnaissance automatique de la parole en coréen avec peu de ressources annotées
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In: Journées d'Etude sur la Parole ; https://hal.archives-ouvertes.fr/hal-01843404 ; Journées d'Etude sur la Parole, Jan 2014, Le Mans, France (2014)
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