Techniques for high quality Arabic speech synthesis
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Software engineering: Systems and tools
Automatic speech recognition and speech variability: A review
Speech Communication
Arabic speech and text in TIDES OnTAP
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Automatic Segmentation and Labeling for Spontaneous Standard Malay Speech Recognition
ICACTE '08 Proceedings of the 2008 International Conference on Advanced Computer Theory and Engineering
IEICE - Transactions on Information and Systems
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Toward enhanced Arabic speech recognition using part of speech tagging
International Journal of Speech Technology
Within-word pronunciation variation modeling for Arabic ASRs: a direct data-driven approach
International Journal of Speech Technology
Statistical analysis of arabic phonemes used in arabic speech recognition
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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One of the problems in the speech recognition of Modern Standard Arabic (MSA) is the cross-word pronunciation variation. Cross-word pronunciation variations alter the phonetic spelling of words beyond their listed forms in the phonetic dictionary, leading to a number of Out-Of-Vocabulary (OOV) wordforms. This paper presents a knowledge-based approach to model cross-word pronunciation variation at both phonetic dictionary and language model levels. The proposed approach is based on modeling cross-word pronunciation variation by expanding the phonetic dictionary and corpus transcription. The Baseline system contains a phonetic dictionary of 14,234 words from a 5.4 hours corpus of Arabic broadcast news. The expanded dictionary contains 15,873 words. Also, the corpus transcription is expanded according to the applied Arabic phonological rules. Using Carnegie Mellon University (CMU) Sphinx speech recognition engine, the Enhanced system achieved Word Error Rate (WER) of 9.91% on a test set of fully discretized transcription of about 1.1 hours of Arabic broadcast news. The WER is enhanced by 2.3% compared to the Baseline system.