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Robust Emotion Recognition using Spectral and Prosodic FeaturesRobust Emotion Recognition using Sentence, Word and Syllable Level Prosodic Features

Robust Emotion Recognition using Spectral and Prosodic Features: Robust Emotion Recognition using... [This chapter discuss about the use of prosodic information in discriminating the emotions. The motivation for exploring the prosodic features to recognize the emotions is illustrated using the gross statistics and time varying patterns of prosodic parameters. Prosodic correlates of speech such as energy, duration and pitch parameters are computed from the emotional utterances. Global prosodic features representing the gross statistics of prosody and local prosodic features representing the finer variations in prosody are introduced in this chapter for discriminating the emotions. These parameters are further extracted separately from different levels such as entire utterances, words and syllables. The analysis of contribution of emotional information by the initial, middle and final portions of sentences, words and syllables are studied. Use of support vector machines for classifying emotional utterances based on prosodic features has been demonstrated. Chapter ends with discussion on emotion recognition results and important conclusions.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Robust Emotion Recognition using Spectral and Prosodic FeaturesRobust Emotion Recognition using Sentence, Word and Syllable Level Prosodic Features

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Publisher
Springer New York
Copyright
© The Author(s) 2013
ISBN
978-1-4614-6359-7
Pages
47 –69
DOI
10.1007/978-1-4614-6360-3_3
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter discuss about the use of prosodic information in discriminating the emotions. The motivation for exploring the prosodic features to recognize the emotions is illustrated using the gross statistics and time varying patterns of prosodic parameters. Prosodic correlates of speech such as energy, duration and pitch parameters are computed from the emotional utterances. Global prosodic features representing the gross statistics of prosody and local prosodic features representing the finer variations in prosody are introduced in this chapter for discriminating the emotions. These parameters are further extracted separately from different levels such as entire utterances, words and syllables. The analysis of contribution of emotional information by the initial, middle and final portions of sentences, words and syllables are studied. Use of support vector machines for classifying emotional utterances based on prosodic features has been demonstrated. Chapter ends with discussion on emotion recognition results and important conclusions.]

Published: Jan 13, 2013

Keywords: Emotion Recognition; Final Word; Pitch Contour; Prosodic Feature; Syllable Duration

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