Summary of the paper
| Title | Development of a TV Broadcasts Speech Recognition System for Qatari Arabic |
|---|---|
| Authors | Mohamed Elmahdy, Mark Hasegawa-Johnson and 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. |
| Topics | Speech Resource/Database, Other |
Full paper  |
Development of a TV Broadcasts Speech Recognition System for Qatari Arabic |
| Bibtex | @InProceedings{ELMAHDY14.430, author = {Mohamed Elmahdy and Mark Hasegawa-Johnson and Eiman Mustafawi}, title = {Development of a TV Broadcasts Speech Recognition System for Qatari Arabic}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |