Novel Techniques for Dialectal Arabic Speech Recognition (Paperback, 2012 ed.)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Release date:	April 2014
First published:	2012
Authors:	Mohamed Elmahdy • Rainer Gruhn • Wolfgang Minker
Dimensions:	235 x 155 x 7mm (L x W x T)
Format:	Paperback
Pages:	110
Edition:	2012 ed.
ISBN-13:	978-1-4899-9945-0
Categories:	Books > Language & Literature > Language & linguistics > Computational linguistics Books > Language & Literature > Literary & linguistic reference works > General Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > General Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General Promotions
LSN:	1-4899-9945-0
Barcode:	9781489999450

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