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- 2012932302 abstract "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-- Source other than Library of Congress.".
- 2012932302 contributor B12697760.
- 2012932302 contributor B12697761.
- 2012932302 created "c2012.".
- 2012932302 date "2012".
- 2012932302 date "c2012.".
- 2012932302 dateCopyrighted "c2012.".
- 2012932302 description "Fundamentals -- Speech Corpora -- Phonemic Acoustic Modeling -- Graphemic Acoustic Modeling -- Phonetic Transcription Using the Arabic Chat Alphabet -- Conclusions and Future Directions.".
- 2012932302 description "Includes bibliographical references (p. 103-107) and index.".
- 2012932302 description "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-- Source other than Library of Congress.".
- 2012932302 extent "xxi, 110 p. :".
- 2012932302 identifier "1461419050 (alk. paper)".
- 2012932302 identifier "9781461419051 (alk. paper)".
- 2012932302 issued "2012".
- 2012932302 issued "c2012.".
- 2012932302 language "eng".
- 2012932302 publisher "New York : Springer,".
- 2012932302 subject "006.454".
- 2012932302 subject "Arabic language.".
- 2012932302 subject "Automatic speech recognition.".
- 2012932302 subject "TK7895.S65 E46 2012".
- 2012932302 tableOfContents "Fundamentals -- Speech Corpora -- Phonemic Acoustic Modeling -- Graphemic Acoustic Modeling -- Phonetic Transcription Using the Arabic Chat Alphabet -- Conclusions and Future Directions.".
- 2012932302 title "Novel techniques for dialectal Arabic speech recognition / Mohamed Elmahdy, Rainer Gruhn, Wolfgang Minker.".
- 2012932302 type "text".