Development of a TV Broadcasts Speech Recognition System for Qatari Arabic

Mohamed Elmahdy, Mark Hasegawa-Johnson, Eiman Mustafawi


Abstract
A major problem with dialectal Arabic speech recognition is due to the sparsity of speech resources. In this paper, a transfer learning framework is proposed to jointly use a large amount of Modern Standard Arabic (MSA) data and little amount of dialectal Arabic data to improve acoustic and language modeling. The Qatari Arabic (QA) dialect has been chosen as a typical example for an under-resourced Arabic dialect. A wide-band speech corpus has been collected and transcribed from several Qatari TV series and talk-show programs. A large vocabulary speech recognition baseline system was built using the QA corpus. The proposed MSA-based transfer learning technique was performed by applying orthographic normalization, phone mapping, data pooling, acoustic model adaptation, and system combination. The proposed approach can achieve more than 28% relative reduction in WER.
Anthology ID:
L14-1369
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3057–3061
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/430_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Mohamed Elmahdy, Mark Hasegawa-Johnson, and Eiman Mustafawi. 2014. Development of a TV Broadcasts Speech Recognition System for Qatari Arabic. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3057–3061, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Development of a TV Broadcasts Speech Recognition System for Qatari Arabic (Elmahdy et al., LREC 2014)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/430_Paper.pdf