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This paper describes a novel Arabic Reading Enhancement Tool (ARET) for classroom use, which has been built using corpus-based Natural Language Processing in combination with expert linguistic annotation. The NLP techniques include a widely used morphological analyzer for Modern Standard Arabic to provide word-level grammatical details, and a rela-tional(More)
Attempts to train a computer to mimic a vocal pathology expert's perception of perceivable voice problems have had limited success. A recent study successfully used a Cepstrum-based calculation (CPPs) to detect dysphonic speech, but it made significant false negative and false positive errors [1]. Appropriate training data could improve calculation(More)
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