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Titlebook: Novel Techniques for Dialectal Arabic Speech Recognition; Mohamed Elmahdy,Rainer Gruhn,Wolfgang Minker Book 2012 Springer Science+Business

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發(fā)表于 2025-3-21 19:53:57 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Novel Techniques for Dialectal Arabic Speech Recognition
編輯Mohamed Elmahdy,Rainer Gruhn,Wolfgang Minker
視頻videohttp://file.papertrans.cn/669/668458/668458.mp4
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
圖書封面Titlebook: Novel Techniques for Dialectal Arabic Speech Recognition;  Mohamed Elmahdy,Rainer Gruhn,Wolfgang Minker Book 2012 Springer Science+Business
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
關鍵詞Arabic dialect; Arabic speech recognition; ECA; ECA speech data; MAP; MLLR; MSA; arabic
版次1
doihttps://doi.org/10.1007/978-1-4614-1906-8
isbn_softcover978-1-4899-9945-0
isbn_ebook978-1-4614-1906-8
copyrightSpringer Science+Business Media New York 2012
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Phonemic Acoustic Modeling,imited research done in phonetic transcription. In this chapter, we address the problem of having only a small amount of dialectal Arabic speech data, for which it is possible to have a phonetic transcription. Initially, a phonemic acoustic model was trained using a large amount of Modern Standard A
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Graphemic Acoustic Modeling,d in the previous chapter. In this chapter, we tackle the problem of having only graphemic transcriptions instead of phonemic ones. The problem is tackled by adopting grapheme-based acoustic modeling. In this approach, phonetic transcription is approximated to be the word letters instead of the exac
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Phonetic Transcription Using the Arabic Chat Alphabet,ude the short vowels that are missing in traditional Arabic orthography. Furthermore, ACA transcriptions can be rapidly prepared, and for some transcribers, it is even more convenient rather than traditional orthography. Furthermore, we propose a phonetic transcription notation that is based on ACA
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g the possible relationship between ovaries and breast cancer was made almost intuitively by ., a German physician, in 1889 and was confirmed in practice several years later (1896) by ., a British surgeon. The latter for the first time performed bilateral oophorectomy in two women with disseminated
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Mohamed Elmahdy,Rainer Gruhn,Wolfgang Minkerution of the assignment problem.Discusses the connection betThis thesis discusses the random Euclidean bipartite matching problem, i.e., the matching problem between two different sets of points randomly generated on the Euclidean domain. The presence of both randomness and Euclidean constraints mak
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Mohamed Elmahdy,Rainer Gruhn,Wolfgang Minkerution of the assignment problem.Discusses the connection betThis thesis discusses the random Euclidean bipartite matching problem, i.e., the matching problem between two different sets of points randomly generated on the Euclidean domain. The presence of both randomness and Euclidean constraints mak
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