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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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Multimodal Emotion Recognition for AVEC 2016 Challenge
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In: Audio/Visual Emotion Challenge ; https://hal.archives-ouvertes.fr/hal-01837203 ; Audio/Visual Emotion Challenge, ACM, Oct 2016, Amsterdam, Netherlands (2016)
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Marginal Contrast Among Romanian Vowels: Evidence from ASR and Functional Load
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In: Interspeech 2016 ; https://hal.archives-ouvertes.fr/hal-01453014 ; Interspeech 2016, ISCA, Sep 2016, San Francisco, United States. pp.2433 - 2437, ⟨10.21437/Interspeech.2016-762⟩ ; http://www.interspeech2016.org/ (2016)
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Réalisation phonétique et contraste phonologique marginal : une étude automatique des voyelles du roumain
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In: JEP 2016 ; https://hal.archives-ouvertes.fr/hal-01452974 ; JEP 2016, Aug 2016, Paris, France (2016)
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BULB: Breaking the Unwritten Language Barrier
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In: Procedia Computer Science ; Computational Methods for Endangered Language Documentation and Description ; https://hal.archives-ouvertes.fr/hal-01836496 ; Computational Methods for Endangered Language Documentation and Description, May 2016, Yogyakarta, Indonesia. pp.8-14, ⟨10.1016/j.procs.2016.04.023⟩ (2016)
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A phonologically weak contrast can induce phonetic overlap
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In: Laboratory Phonology Conference ; https://hal.archives-ouvertes.fr/hal-01837204 ; Laboratory Phonology Conference, Jul 2016, Ithaca, United States (2016)
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Breaking the unwritten language barrier: the BULB project
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In: SLTU-2016 5th Workshop on Spoken Language Technologies for Under-resourced languages ; https://halshs.archives-ouvertes.fr/halshs-01428027 ; SLTU-2016 5th Workshop on Spoken Language Technologies for Under-resourced languages, May 2016, Yogyakarta, Indonesia. ⟨10.1016/j.procs.2016.04.023⟩ (2016)
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Innovative technologies for under-resourced language documentation: The BULB Project
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In: CCURL proceedings ; Workshop CCURL 2016 - Collaboration and Computing for Under-Resourced Languages - LREC ; https://hal.archives-ouvertes.fr/hal-01350124 ; Workshop CCURL 2016 - Collaboration and Computing for Under-Resourced Languages - LREC, May 2016, Portoroz, Slovenia (2016)
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Improving Data Selection for Low Resource STT and KWS
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Abstract:
This paper extends recent research on training data selection for speech transcription and keyword spotting system development. Selection techniques were explored in the context of the IARPA-Babel Active Learning (AL) task for 6 languages. Different selection criteria were considered with the goal of improving over a system built using a pre-defined 3-hour training data set. Four variants of the entropy-based criterion were explored: words, triphones, phones as well as the use of HMM-states previously introduced in [4]. The influence of the number of HMM-states was assessed as well as whether automatic or manual reference transcripts were used. The combination of selection criteria was investigated, and a novel multi-stage selection method proposed. This method was also assessed using larger data sets than were permitted in the Babel AL task. Results are reported for the 6 languages. The multi-stage selection was also applied to the surprise language (Swahili) in the NIST OpenKWS 2015 evaluation. ; 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) , 13 Dec 2015, 17 Dec 2015
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Keyword:
acoustics; data selection; decoding; entropy; Hidden Markov models; IARPA Collection; keyword spotting; low-resource languages; speech; speech recognition; training; training data
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URL: http://www.dtic.mil/docs/citations/AD1038536 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=AD1038536
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Machine Translation Based Data Augmentation for Cantonese Keyword Spotting (Author's Manuscript)
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Investigating Techniques for Low Resource Conversational Speech Recognition
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Breaking the unwritten language barrier: the BULB project
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In: SLTU-2016 5th Workshop on Spoken Language Technologies for Under-resourced languages ; https://halshs.archives-ouvertes.fr/halshs-01428027 ; SLTU-2016 5th Workshop on Spoken Language Technologies for Under-resourced languages, May 2016, Yogyakarta, Indonesia. ⟨10.1016/j.procs.2016.04.023⟩ (2016)
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Innovative technologies for under-resourced language documentation: The BULB Project
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In: CCURL proceedings ; Workshop CCURL 2016 - Collaboration and Computing for Under-Resourced Languages - LREC ; https://hal.archives-ouvertes.fr/hal-01350124 ; Workshop CCURL 2016 - Collaboration and Computing for Under-Resourced Languages - LREC, May 2016, Portoroz, Slovenia (2016)
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BULB: Breaking the Unwritten Language Barrier
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In: Procedia Computer Science ; Computational Methods for Endangered Language Documentation and Description ; https://hal.archives-ouvertes.fr/hal-01836496 ; Computational Methods for Endangered Language Documentation and Description, May 2016, Yogyakarta, Indonesia. pp.8-14, ⟨10.1016/j.procs.2016.04.023⟩ (2016)
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