000 | 03386nam a22005415i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/978-1-4614-5143-3 _2doi |
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050 | 4 | _aTK5102.9 | |
050 | 4 | _aTA1637-1638 | |
050 | 4 | _aTK7882.S65 | |
072 | 7 |
_aTTBM _2bicssc |
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072 | 7 |
_aUYS _2bicssc |
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072 | 7 |
_aTEC008000 _2bisacsh |
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072 | 7 |
_aCOM073000 _2bisacsh |
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082 | 0 | 4 |
_a621.382 _223 |
100 | 1 |
_aRao, K. Sreenivasa. _eauthor. |
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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. |
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300 |
_aXII, 124 p. 30 illus., 6 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSpringerBriefs in Electrical and Computer Engineering, SpringerBriefs in Speech Technology, _x2191-8112 |
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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 |