Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Maximum entropy based restoration of Arabic diacritics
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
MAGEAD: a morphological analyzer and generator for the Arabic dialects
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Arabic diacritization through full morphological tagging
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Spoken Arabic dialect identification using phonotactic modeling
Semitic '09 Proceedings of the EACL 2009 Workshop on Computational Approaches to Semitic Languages
Automatic diacritization of Arabic for acoustic modeling in speech recognition
Semitic '04 Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages
Spoken Arabic dialect identification using phonotactic modeling
Semitic '09 Proceedings of the EACL 2009 Workshop on Computational Approaches to Semitic Languages
Cross-word Arabic pronunciation variation modeling for speech recognition
International Journal of Speech Technology
Within-word pronunciation variation modeling for Arabic ASRs: a direct data-driven approach
International Journal of Speech Technology
| Hi-index | 0.00 |
In this paper, we show that linguistically motivated pronunciation rules can improve phone and word recognition results for Modern Standard Arabic (MSA). Using these rules and the MADA morphological analysis and disambiguation tool, multiple pronunciations per word are automatically generated to build two pronunciation dictionaries; one for training and another for decoding. We demonstrate that the use of these rules can significantly improve both MSA phone recognition and MSA word recognition accuracies over a baseline system using pronunciation rules typically employed in previous work on MSA Automatic Speech Recognition (ASR). We obtain a significant improvement in absolute accuracy in phone recognition of 3.77%--7.29% and a significant improvement of 4.1% in absolute accuracy in ASR.