Cross-Word Modeling for Arabic Speech Recognition by Dia AbuZeina

By Dia AbuZeina

Cross-Word Modeling for Arabic Speech Recognition makes use of phonological ideas so that it will version the cross-word challenge, a merging of adjoining phrases in speech because of non-stop speech, to augment the functionality of constant speech reputation structures. the writer goals to supply an knowing of the cross-word challenge and the way it may be kept away from, in particular concentrating on Arabic phonology utilizing an HHM-based classifier.

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Cross-Word Modeling for Arabic Speech Recognition

Cross-Word Modeling for Arabic Speech popularity makes use of phonological principles as a way to version the cross-word challenge, a merging of adjoining phrases in speech because of non-stop speech, to augment the functionality of continuing speech reputation platforms. the writer goals to supply an knowing of the cross-word challenge and the way it may be refrained from, particularly concentrating on Arabic phonology utilizing an HHM-based classifier.

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Then, an algorithm to model this problem is provided. 1 Introduction Arabic speech recognition has gained increasing importance in the last few years. Speech recognition systems are often used as the front-end for many natural language processing (NLP) applications. These typical applications include voice dialing, call routing, data entry and dictation, information retrieval and extraction, command and control, computer-aided language learning, machine translation, etc. In fact, speech communication with computers is envisioned to be the dominant human–machine interface in the near future.

Arabic is a Semitic language spoken by more than 330 million people as a native language (Farghaly and Shaalan 2009). In this book, we focus on the modern standard Arabic (MSA) which is currently used in writing and most formal speech. MSA is the major medium of communication for public speaking and news broadcasting (Ryding 2005). The close relation between the Holy Qur’an and MSA phonological rules has helped to preserve MSA and its rules. The crossword problem presented in the previous chapter will be modeled using MSA phonological rules.

V>: Vowels (Fatha, Damma, Kasra, and Shadda). : Vowels without Shadda (Fatha, Damma, and Kasra).

: Prefix letters (waaw, baa’, faa’, kaaf, and laam). : Emphatic letters (Tah, Saad, Daad, and Zaa’). : Pharyngeal letters (qaaf, ghayn, khaa’, and raa’). <¼pattern): context before the current position matches the pattern. pattern): context before the current position does not match the pattern. ¼pattern): context after the current position matches the pattern. pattern): context after the current position does not match the pattern.

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