Titlebook: Cross-Word Modeling for Arabic Speech Recognition; Dia AbuZeina,Moustafa Elshafei Book 2012 Dia AbuZeina 2012 Arabic linguistics.Arabic ph

JADE

書(shū)目名稱Cross-Word Modeling for Arabic Speech Recognition影響因子(影響力)




書(shū)目名稱Cross-Word Modeling for Arabic Speech Recognition影響因子(影響力)學(xué)科排名




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書(shū)目名稱Cross-Word Modeling for Arabic Speech Recognition讀者反饋




書(shū)目名稱Cross-Word Modeling for Arabic Speech Recognition讀者反饋學(xué)科排名

善于

言行自由

Reconstructing Teachers of MathematicsThis chapter presents a brief overview of the evolution of Arabic speech recognition systems. It provides a literature survey of Arabic speech recognition systems and discusses some of the challenges of Arabic from the speech recognition point of view.

Deadpan

BROW

Reconstructing Teachers of MathematicsThis chapter discusses some Arabic phonological rules that can be used to capture the variations occurring at words’ junctures. The rules include Idgham, Iqlaab, Hamzat Al-Wasl, and merging of two consecutive unvoweled letters. Then, an algorithm to model this problem is provided.

皺痕

Wetlands: A Multidisciplinary PerspectiveThis chapter presents the results achieved by modeling cross-word pronunciation variation problem of MSA. We practically investigated two MSA phonological rules (Idgham and Iqlaab) which significantly enhanced recognition accuracy. Three ASR’s metrics were measured: word error rate (WER), out of vocabulary (OOV), and perplexity (PP).

皺痕

Arabic Speech Recognition Systems,This chapter presents a brief overview of the evolution of Arabic speech recognition systems. It provides a literature survey of Arabic speech recognition systems and discusses some of the challenges of Arabic from the speech recognition point of view.

偽書(shū)

Cross-Word Pronunciation Variations,This chapter presents the cross-word problem of the Arabic language. It also includes the main sources of this problem: Idgham (merging), Iqlaab (changing), Hamzat Al-Wasl deleting, and merging of two consecutive unvoweled letters. Illustrative examples of the cross-word problem are also provided.

抵制

Modeling of Arabic Cross-Word Pronunciation Variations,This chapter discusses some Arabic phonological rules that can be used to capture the variations occurring at words’ junctures. The rules include Idgham, Iqlaab, Hamzat Al-Wasl, and merging of two consecutive unvoweled letters. Then, an algorithm to model this problem is provided.

ANIM

Performance and Evaluation,This chapter presents the results achieved by modeling cross-word pronunciation variation problem of MSA. We practically investigated two MSA phonological rules (Idgham and Iqlaab) which significantly enhanced recognition accuracy. Three ASR’s metrics were measured: word error rate (WER), out of vocabulary (OOV), and perplexity (PP).