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Automatic Identification of Arabic Dialects Using Hidden Markov Models

Automatic Identification of Arabic Dialects Using Hidden Markov Models


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About the Book

The Arabic language has many different dialects, they must be identified before Automatic Speech Recognition can take place. This thesis examines the difficult task of properly identifying various Arabic dialects. We present a novel design of an Arabic dialect identification system using Hidden Markov Models (HMM). Due to the similarities and the differences among Arabic dialects, we build a ergodic HMM that has two types of states; one of them represents the common sounds across Arabic dialects, while the other represents the unique sounds of the specific dialect. We tie the common states across all models since they share the same sounds. We focus only on two major dialects: Egyptian and Gulf. An improved initialization process is used to achieve better Arabic dialect identification. Moreover, we utilize many different combinations of speech features related to MFCC such as time derivatives, energy, and the Shifted Delta Cepstra in training and testing the system. We present a detailed comparison of the performance of our Arabic dialect identification system using the different combinations. The best result of the Arabic dialect identification system is 96.67% correct identification. Keywords. Language Identification, Gaussian Mixture Models, GMM, Egyptian Dialect, Gulf Dialect, Arabic Dialect Database, HMM Initializations, Jackknifing.


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Product Details
  • ISBN-13: 9781243563460
  • Publisher: Proquest, Umi Dissertation Publishing
  • Publisher Imprint: Proquest, Umi Dissertation Publishing
  • Height: 254 mm
  • Weight: 281 gr
  • ISBN-10: 124356346X
  • Publisher Date: 01 Sep 2011
  • Binding: Paperback
  • Spine Width: 9 mm
  • Width: 203 mm


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Automatic Identification of Arabic Dialects Using Hidden Markov Models
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Automatic Identification of Arabic Dialects Using Hidden Markov Models
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