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Vocapia-LIMSI System for 2020 Shared Task on Code-switched Spoken Language Identification
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In: The First Workshop on Speech Technologies for Code-Switching in Multilingual Communities ; https://hal.archives-ouvertes.fr/hal-03091792 ; The First Workshop on Speech Technologies for Code-Switching in Multilingual Communities, Oct 2020, Shanghai, China (2020)
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Challenges in Audio Processing of Terrorist-Related Data
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In: International Conference on Multimedia Modeling ; https://hal.archives-ouvertes.fr/hal-02415176 ; International Conference on Multimedia Modeling, Springer, Jan 2019, Thessaloniki, Greece (2019)
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Challenges in Audio Processing of Terrorist-Related Data
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In: International Conference on Multimedia Modeling ; https://hal.archives-ouvertes.fr/hal-02387373 ; International Conference on Multimedia Modeling, Springer, Jan 2019, Thessaloniki, Greece (2019)
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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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Lexical speaker identification in TV shows
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In: ISSN: 1380-7501 ; EISSN: 1573-7721 ; Multimedia Tools and Applications ; https://hal.archives-ouvertes.fr/hal-01690342 ; Multimedia Tools and Applications, Springer Verlag, 2015, 74 (4), pp.1377 - 1396. ⟨10.1007/s11042-014-1940-3⟩ (2015)
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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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Comparing decoding strategies for subword-based keyword spotting in low-resourced languages
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In: Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-01843408 ; Annual Conference of the International Speech Communication Association , ISCA, Sep 2014, Singapore, Singapore (2014)
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Abstract:
International audience ; For languages with limited training resources, out-of-vocabulary (OOV) words are a significant problem, both fortranscription and keyword spotting. This paper investigates theuse of subword lexical units for keyword spotting. Three strate-gies for using the sub-word units are explored: 1) convertingword-based lattices to subword lattices after decoding, 2) per-forming a separate decoding for each subword type, and 3) asingle decoding using all possible subword units. In these ex-periments, the best performance is achieved by carrying out aseparate decoding for each subword type. Further gains are at-tained through system combination. We also find that ignor-ing word boundaries improves the detection of OOV keywordswithout significantly impacting in-vocabulary keyword detec-tion. Results are presented on four languages from the IARPABabel Program (Haitian Creole, Assamese, Bengali, and Zulu).
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs]; keyword search; low resource LVCSR; OOV; spoken term detection; sub-word lexical units
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URL: https://hal.archives-ouvertes.fr/hal-01843408
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