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010   $a3-319-49220-9
024 7 $a10.1007/978-3-319-49220-9
035   $a(CKB)3710000001019198
035   $a(DE-He213)978-3-319-49220-9
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035   $a(PPN)198341083
035   $a(EXLCZ)993710000001019198
100   $a20170111d2017      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aSpeech Recognition Using Articulatory and Excitation Source Features /$fby K. Sreenivasa Rao, Manjunath K E
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (XI, 92 p. 23 illus., 4 illus. in color.)
225 1 $aSpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,$x2191-737X
311   $a3-319-49219-5 
320   $aIncludes bibliographical references at the end of each chapters.
327   $aIntroduction -- Literature Review -- Articulatory Features for Phone Recognition -- Excitation Source Features for Phone Recognition -- Articulatory and Excitation Source Features for Speech Recognition in Read, Extempore and Conversation Modes -- Conclusion -- Appendix A: MFCC Features -- Appendix B: Pattern Recognition Models.
330   $aThis book discusses the contribution of articulatory and excitation source information in discriminating sound units. The authors focus on excitation source component of speech -- and the dynamics of various articulators during speech production -- for enhancement of speech recognition (SR) performance. Speech recognition is analyzed for read, extempore, and conversation modes of speech. Five groups of articulatory features (AFs) are explored for speech recognition, in addition to conventional spectral features. Each chapter provides the motivation for exploring the specific feature for SR task, discusses the methods to extract those features, and finally suggests appropriate models to capture the sound unit specific knowledge from the proposed features. The authors close by discussing various combinations of spectral, articulatory and source features, and the desired models to enhance the performance of SR systems.
410  0$aSpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,$x2191-737X
606   $aSignal processing
606   $aImage processing
606   $aSpeech processing systems
606   $aNatural language processing (Computer science)
606   $aComputational linguistics
606   $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051
606   $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040
606   $aComputational Linguistics$3https://scigraph.springernature.com/ontologies/product-market-codes/N22000
615  0$aSignal processing.
615  0$aImage processing.
615  0$aSpeech processing systems.
615  0$aNatural language processing (Computer science)
615  0$aComputational linguistics.
615 14$aSignal, Image and Speech Processing.
615 24$aNatural Language Processing (NLP).
615 24$aComputational Linguistics.
676   $a152.15
700   $aRao$b K. Sreenivasa$g(Krothapalli Sreenivasa)$4aut$4http://id.loc.gov/vocabulary/relators/aut$01614710
702   $aK E$b Manjunath$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910159386303321
996   $aSpeech Recognition Using Articulatory and Excitation Source Features$94003483
997   $aUNINA