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
- 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:
- 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)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/430_Paper.pdf