Článek ve sborníku konference

VESELÝ Karel, BASKAR Murali K., DIEZ Sánchez Mireia a BENEŠ Karel. MGB-3 but system: Low-resource ASR on Egyptian YouTube data. In: Proceedings of ASRU 2017. Okinawa: IEEE Signal Processing Society, 2017, s. 368-373. ISBN 978-1-5090-4788-8.
Jazyk publikace:angličtina
Název publikace:MGB-3 but system: Low-resource ASR on Egyptian YouTube data
Název (cs):MGB-3 BUT Systém: egyptské rozpoznávání řeči s omezenými zdroji
Sborník:Proceedings of ASRU 2017
Konference:2017 IEEE Automatic Speech Recognition and Understanding Workshop
Místo vydání:Okinawa, JP
Vydavatel:IEEE Signal Processing Society
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2017/vesely_asru2017_mgb3-paper.pdf [PDF]
Klíčová slova
MGB-3, ASR adaptation, low-resource ASR, Egyptian Arabic, diarization
Článek pojednává o strategiích adaptace, které byly použity v MGB-3 evaluacích. Tým VUT použil automatický systém rozpoznávání řeči s omezenými zdroji a aplikoval jej na egyptská YOUTOUBE data.
This paper presents a series of experiments we performed during our work on the MGB-3 evaluations. We both describe the submitted system, as well as the post-evaluation analysis. Our initial BLSTM-HMM system was trained on 250 hours of MGB-2 data (Al-Jazeera), it was adapted with 5 hours of Egyptian data (YouTube). We included such techniques as diarization, n-gram language model adaptation, speed perturbation of the adaptation data, and the use of all 4 correct references. The 4 references were either used for supervision with a confusion network, or we included each sentence 4x with the transcripts from all the annotators. Then, it was also helpful to blend the augmented MGB-3 adaptation data with 15 hours of MGB-2 data. Although we did not rank with our single system among the best teams in the evaluations, we believe that our analysis will be highly interesting not only for the other MGB-3 challenge participants.
   author = {Karel Vesel{\'{y}} and K. Murali Baskar and Mireia
	S{\'{a}}nchez Diez and Karel Bene{\v{s}}},
   title = {MGB-3 but system: Low-resource ASR on Egyptian
	YouTube data},
   pages = {368--373},
   booktitle = {Proceedings of ASRU 2017},
   year = 2017,
   location = {Okinawa, JP},
   publisher = {IEEE Signal Processing Society},
   ISBN = {978-1-5090-4788-8},
   doi = {10.1109/ASRU.2017.8268959},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.cs?id=11595}

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