Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/79851
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dc.contributor.authorXiao, Xiongen
dc.contributor.authorNwe, Tin Layen
dc.contributor.authorChng, Eng Siongen
dc.contributor.authorMa, Binen
dc.contributor.authorLi, Haizhouen
dc.contributor.authorWang, Leien
dc.contributor.authorNi, Chongjiaen
dc.contributor.authorLeung, Cheung-Chien
dc.contributor.authorYou, Changhuaien
dc.contributor.authorXie, Leien
dc.contributor.authorXu, Haihuaen
dc.date.accessioned2019-05-22T05:09:30Zen
dc.date.accessioned2019-12-06T13:35:20Z-
dc.date.available2019-05-22T05:09:30Zen
dc.date.available2019-12-06T13:35:20Z-
dc.date.issued2016en
dc.identifier.citationWang, L., Ni, C., Leung, C. -C., You, C., Xie, L., Xu, H., . . . Li, H. (2016). The NNi Vietnamese speech recognition system for mediaeval 2016. Multimedia Benchmark Workshop, 1739.en
dc.identifier.urihttps://hdl.handle.net/10356/79851-
dc.description.abstractThis paper provides an overall description of the Vietnamese speech recognition system developed by the joint team for MediaEval 2016. The submitted system consisted of 3 subsystems, and adopted different deep neural network-based techniques such as fMLLR transformed bottleneck features, sequence training, etc. Besides the acoustic modeling techniques, speech data augmentation was also examined to develop a more robust acoustic model. The I2R team collected a number of text resources from the Internet and made them available to other participants in the task. The web text crawled from the Internet was used to train a 5-gram language model. The submitted system obtained the token error rate (TER) of 15.1, 23.0 and 50.5 on Devel local set, Devel set and Test set, respectively.en
dc.format.extent3 p.en
dc.language.isoenen
dc.rights© 2016 The Author(s).en
dc.subjectVietnameseen
dc.subjectRecognitionen
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleThe NNi Vietnamese speech recognition system for mediaeval 2016en
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.contributor.conferenceMultimedia Benchmark Workshopen
dc.description.versionPublished versionen
dc.identifier.urlhttp://ceur-ws.org/Vol-1739/MediaEval_2016_paper_52.pdfen
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