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130626 ||| eng |
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|a 9781461463603
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|a Rao, K. Sreenivasa
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| 245 |
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|a Robust Emotion Recognition using Spectral and Prosodic Features
|h Elektronische Ressource
|c by K. Sreenivasa Rao, Shashidhar G. Koolagudi
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| 250 |
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|a 1st ed. 2013
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| 260 |
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|a New York, NY
|b Springer New York
|c 2013, 2013
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| 300 |
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|a XII, 118 p. 37 illus., 15 illus. in color
|b online resource
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| 505 |
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|a Introduction -- Robust Emotion Recognition using Pitch Synchronous and Sub-syllabic Spectral Features -- Robust Emotion Recognition using Word and Syllable Level Prosodic Features -- Robust Emotion Recognition using Combination of Excitation Source, Spectral and Prosodic Features -- Robust Emotion Recognition using Speaking Rate Features -- Emotion Recognition on Real Life Emotions -- Summary and Conclusions -- MFCC Features -- Gaussian Mixture Model (GMM)
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| 653 |
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|a User interfaces (Computer systems)
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| 653 |
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|a Computational Linguistics
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| 653 |
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|a Computational linguistics
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| 653 |
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|a Signal, Speech and Image Processing
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| 653 |
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|a Natural Language Processing (NLP)
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| 653 |
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|a Signal processing
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| 653 |
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|a User Interfaces and Human Computer Interaction
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| 653 |
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|a Human-computer interaction
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| 653 |
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|a Natural language processing (Computer science)
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| 700 |
1 |
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|a Koolagudi, Shashidhar G.
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning
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| 028 |
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|a 10.1007/978-1-4614-6360-3
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| 856 |
4 |
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|u https://doi.org/10.1007/978-1-4614-6360-3?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 621.382
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| 520 |
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|a In this brief, the authors discuss recently explored spectral (sub-segmental and pitch synchronous) and prosodic (global and local features at word and syllable levels in different parts of the utterance) features for discerning emotions in a robust manner. The authors also delve into the complementary evidences obtained from excitation source, vocal tract system and prosodic features for the purpose of enhancing emotion recognition performance. Features based on speaking rate characteristics are explored with the help of multi-stage and hybrid models for further improving emotion recognition performance. Proposed spectral and prosodic features are evaluated on real life emotional speech corpus
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