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001 978-1-4614-5143-3
003 DE-He213
005 20200420220225.0
007 cr nn 008mamaa
008 121116s2013 xxu| s |||| 0|eng d
020 _a9781461451433
_9978-1-4614-5143-3
024 7 _a10.1007/978-1-4614-5143-3
_2doi
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 _aRao, K. Sreenivasa.
_eauthor.
245 1 0 _aEmotion Recognition using Speech Features
_h[electronic resource] /
_cby K. Sreenivasa Rao, Shashidhar G. Koolagudi.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2013.
300 _aXII, 124 p. 30 illus., 6 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Electrical and Computer Engineering, SpringerBriefs in Speech Technology,
_x2191-8112
505 0 _aIntroduction -- Speech Emotion Recognition: A Review -- Emotion Recognition Using Excitation Source Information -- Emotion Recognition Using Vocal Tract Information -- Emotion Recognition Using Prosodic Information -- Summary and Conclusions -- Linear Prediction Analysis of Speech -- MFCC Features -- Gaussian Mixture Model (GMM).
520 _a"Emotion Recognition Using Speech Features" covers emotion-specific features present in speech and discussion of suitable models for capturing emotion-specific information for distinguishing different emotions.  The content of this book is important for designing and developing  natural and sophisticated speech systems. Drs. Rao and Koolagudi lead a discussion of how emotion-specific information is embedded in speech and how to acquire emotion-specific knowledge using appropriate statistical models. Additionally, the authors provide information about using evidence derived from various features and models. The acquired emotion-specific knowledge is useful for synthesizing emotions. Discussion includes global and local prosodic features at syllable, word and phrase levels, helpful for capturing emotion-discriminative information; use of complementary evidences obtained from excitation sources, vocal tract systems and prosodic features in order to enhance the emotion recognition performance;  and proposed multi-stage and hybrid models for improving the emotion recognition performance.
650 0 _aEngineering.
650 0 _aUser interfaces (Computer systems).
650 0 _aComputational linguistics.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aUser Interfaces and Human Computer Interaction.
650 2 4 _aComputational Linguistics.
700 1 _aKoolagudi, Shashidhar G.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461451426
830 0 _aSpringerBriefs in Electrical and Computer Engineering, SpringerBriefs in Speech Technology,
_x2191-8112
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-5143-3
912 _aZDB-2-ENG
942 _cEBK
999 _c52147
_d52147