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MAGIC DUST FOR CROSS-LINGUAL ADAPTATION OF MONOLINGUAL WAV2VEC-2.0
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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
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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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Transdisciplinary Analysis of a Corpus of French Newsreels: The ANTRACT Project
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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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Where are we in Named Entity Recognition from Speech?
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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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A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning
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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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A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning ...
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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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Investigating Techniques for Low Resource Conversational Speech Recognition
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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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Abstract:
International audience ; This paper investigates the development of a speech-to-text transcription system for the Korean language in the context of the DGA RAPID Rapmat project. Korean is an alpha-syllabary language spoken by about 78 million people worldwide. As only a small amount of manually transcribed audio data were available, the acoustic models were trained on audio data downloaded from several Korean websites in an unsupervised manner, and the language models were trained on web texts. The reported word and character error rates are estimates, as development corpus used in these experiments was also constructed from the untranscribed audio data, the web texts and automatic transcriptions. Several variants for unsupervised acoustic model training were compared to assess the influence of the vocabulary size (200k vs 2M), the type of language model (words vs characters), the acoustic unit (phonemes vs half-syllables), as well as incremental batch vs iterative decoding of the untranscribed audio corpus.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs]; approximative transcripts; korean; Speech recognition system; unsupervised acoustic training
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URL: https://hal.archives-ouvertes.fr/hal-01843405
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