000 03339nam a22006015i 4500
001 978-1-4614-1906-8
003 DE-He213
005 20141014113451.0
007 cr nn 008mamaa
008 120210s2012 xxu| s |||| 0|eng d
020 _a9781461419068
_9978-1-4614-1906-8
024 7 _a10.1007/978-1-4614-1906-8
_2doi
041 _aeng
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
072 7 _aTTBM
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aCOM073000
_2bisacsh
082 0 4 _a621.382
_223
100 1 _aElmahdy, Mohamed.
_eauthor.
245 1 0 _aNovel Techniques for Dialectal Arabic Speech Recognition
_h[electronic resource] /
_cby Mohamed Elmahdy, Rainer Gruhn, Wolfgang Minker.
260 1 _aBoston, MA :
_bSpringer US,
_c2012.
264 1 _aBoston, MA :
_bSpringer US,
_c2012.
300 _aXXII, 110 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aFundamentals -- Speech Corpora -- Phonemic Acoustic Modeling -- Graphemic Acoustic Modeling -- Phonetic Transcription Using the Arabic Chat Alphabet.
520 _aNovel 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.
650 0 _aEngineering.
650 0 _aTranslators (Computer programs).
650 0 _aArabic languages.
650 0 _aComputational linguistics.
650 0 _aTelecommunication.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aLanguage Translation and Linguistics.
650 2 4 _aCommunications Engineering, Networks.
650 2 4 _aArabic.
650 2 4 _aComputational Linguistics.
700 1 _aGruhn, Rainer.
_eauthor.
700 1 _aMinker, Wolfgang.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461419051
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-1906-8
912 _aZDB-2-ENG
942 _cEB
999 _c1970
_d1970