Using Suprasegmentals in Training Hidden Markov Models For Arabic
نویسنده
چکیده
Automatic speech segmentation is an essential tool for building large corpora for training continuous speech recognition systems. Manual segmentation of speech is both time consuming and an error-prone task. Several automatic segmentation systems have been proposed based on the acoustical features of the speech 5] 11]. In this paper, we present a novel technique for automatic seg-mentation of Arabic speech in which both prosodic and acoustical features of the speech are examined to achieve a higher accuracy of segmentation. The system was used to automatically label 1012 utterances of Koranic Arabic. These utterances were then used to train discrete density Hiddem Markov Models (HMM). The resulting models were test on 105 manually segmented utterances. Koranic Arabic speech is the rhythmic speech used in reciting the Koran and is considered the standards for Modern Standard Arabic (MSA) by most Arabic linguists. We show that incorporating the prosodic features in the design resulted in better segmentation accuracy.
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