Novel Techniques for Dialectal Arabic Speech Recognition

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Springer Science & Business Media, Feb 10, 2012 - Technology & Engineering - 110 pages
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Novel Techniques for Dialectal Arabic Speech describes approaches to improve automatic speech recognition for dialectal Arabic. Since speech resources for dialectal Arabic speech recognition are very sparse, the authors describe how existing Modern Standard Arabic (MSA) speech data can be applied to dialectal Arabic speech recognition, while assuming that MSA is always a second language for all Arabic speakers.

In this book, Egyptian Colloquial Arabic (ECA) has been chosen as a typical Arabic dialect. ECA is the first ranked Arabic dialect in terms of number of speakers, and a high quality ECA speech corpus with accurate phonetic transcription has been collected. MSA acoustic models were trained using news broadcast speech. In order to cross-lingually use MSA in dialectal Arabic speech recognition, the authors have normalized the phoneme sets for MSA and ECA. After this normalization, they have applied state-of-the-art acoustic model adaptation techniques like Maximum Likelihood Linear Regression (MLLR) and Maximum A-Posteriori (MAP) to adapt existing phonemic MSA acoustic models with a small amount of dialectal ECA speech data. Speech recognition results indicate a significant increase in recognition accuracy compared to a baseline model trained with only ECA data.

 

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The book proposes useful approaches for dialectal Arabic ASR....

Contents

Introduction
1
Fundamentals
7
Speech Corpora
25
Phonemic Acoustic Modeling
33
Graphemic Acoustic Modeling
53
Phonetic Transcription Using the Arabic Chat Alphabet
71
Conclusions and Future Directions
81
Buckwalter Transliteration
87
Egyptian Colloquial Arabic Lexicon
89
Seinnheiser ME3 Specifications
99
Buddy 6G USB Specifications
101
References
103
Index
108
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