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Robust Emotion Recognition using Spectral and Prosodic FeaturesRobust Emotion Recognition using Combination of Excitation Source, Spectral and Prosodic Features

Robust Emotion Recognition using Spectral and Prosodic Features: Robust Emotion Recognition using... [Different speech features may offer emotion specific information in different ways. This chapter explores the combination evidences offered by various speech features. In this chapter, we consider excitation source, spectral and prosodic features as specific individual speech features for classifying the emotions. Various combinations of the above mentioned individual features are explored for improving the emotion recognition performance. Since, the features are derived from different levels, the emotion specific characteristics captured by these features may be complementary or non-overlapping in nature. By properly exploiting these evidences, the recognition performance will definitely improved. From the results, its is observed that all the combinations explored in this have enhanced the recognition performance significantly.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Robust Emotion Recognition using Spectral and Prosodic FeaturesRobust Emotion Recognition using Combination of Excitation Source, Spectral and Prosodic Features

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Publisher
Springer New York
Copyright
© The Author(s) 2013
ISBN
978-1-4614-6359-7
Pages
71 –84
DOI
10.1007/978-1-4614-6360-3_4
Publisher site
See Chapter on Publisher Site

Abstract

[Different speech features may offer emotion specific information in different ways. This chapter explores the combination evidences offered by various speech features. In this chapter, we consider excitation source, spectral and prosodic features as specific individual speech features for classifying the emotions. Various combinations of the above mentioned individual features are explored for improving the emotion recognition performance. Since, the features are derived from different levels, the emotion specific characteristics captured by these features may be complementary or non-overlapping in nature. By properly exploiting these evidences, the recognition performance will definitely improved. From the results, its is observed that all the combinations explored in this have enhanced the recognition performance significantly.]

Published: Jan 13, 2013

Keywords: Emotion Recognition; Recognition Performance; Vocal Tract; Speech Feature; Prosodic Feature

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