By Dia AbuZeina
Cross-Word Modeling for Arabic Speech Recognition makes use of phonological principles so one can version the cross-word challenge, a merging of adjoining phrases in speech as a result of non-stop speech, to reinforce the functionality of constant speech attractiveness platforms. the writer goals to supply an figuring out of the cross-word challenge and the way it may be refrained from, in particular concentrating on Arabic phonology utilizing an HHM-based classifier.
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Extra info for Cross-Word Modeling for Arabic Speech Recognition
For English, Weintraub et al. (1989) described performance improvements arising from detailed phonological modeling and from the incorporation of cross-word coarticulatory constraints. Giachin et al. (1991) demonstrated that the phonological rules are effective in providing corrective capability at low computational cost. Eleven phonological rules were implemented to handle coarticulation at word junctures. Beulen et al. (1998) presented an application of pronunciation variants in combination with phrases for a large vocabulary, continuous speech recognition system.
2 Iqlaab Iqlaab is a replacement of Nuun Saakinah ( ) or Tanween that comes before voweled Baa ( )ﺏby Meem Saakinah ( ). The following are examples of Iqlaab. Note that instead of geminating the connecting letter, it is unvoweled ( ).
Boulianne et al. (2000) demonstrated finite-state transducers based approach to integrate automatic pronunciation rules and cross-word phenomena for French large vocabulary recognition systems. In addition to the phonological rules, cross-word problem can be modeled using a data-driven approach. In this approach, no linguistic rules are used. It depends on training corpus transcription to merge words, such as merging small words. Sloboda and Waibel (1996) proposed to augment short words to create compound words for German speech recognition.
Cross-Word Modeling for Arabic Speech Recognition by Dia AbuZeina