| 书目名称 | Novel Techniques for Dialectal Arabic Speech Recognition |
| 编辑 | Mohamed Elmahdy,Rainer Gruhn,Wolfgang Minker |
| 视频video | |
| 概述 | Presents novel approaches that overcome the major problems in dialectal Arabic speech recognition.Investigates how to benefit from existing standard Arabic speech resources to improve speech recogniti |
| 图书封面 |  |
| 描述 | .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 |
| 出版日期 | Book 2012 |
| 关键词 | Arabic dialect; Arabic speech recognition; ECA; ECA speech data; MAP; MLLR; MSA; arabic |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4614-1906-8 |
| isbn_softcover | 978-1-4899-9945-0 |
| isbn_ebook | 978-1-4614-1906-8 |
| copyright | Springer Science+Business Media New York 2012 |
发表于 2025-3-21 19:53:57
