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Social Emotions in Nature and Artifact$
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Jonathan Gratch and Stacy Marsella

Print publication date: 2013

Print ISBN-13: 9780195387643

Published to Oxford Scholarship Online: January 2014

DOI: 10.1093/acprof:oso/9780195387643.001.0001

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Toward Effective Automatic Recognition Systems of Emotion in Speech

Toward Effective Automatic Recognition Systems of Emotion in Speech

Chapter:
(p.110) 7 Toward Effective Automatic Recognition Systems of Emotion in Speech
Source:
Social Emotions in Nature and Artifact
Author(s):

Carlos Busso

Murtaza Bulut

Shrikanth Narayanan

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195387643.003.0008

Speech is arguably the most important expressive communication modality. It conveys emotional cues that are essential for enriching descriptions of human and interaction and for enabling human computer interfaces that can facilitate a natural and pleasant interaction. This chapter addresses the main aspects that need to be considered in designing an effective automatic speech emotion recognition (EASER) system. It describes the challenges in collecting databases and annotating the underlying emotional content, summarizes the commonly used acoustic features, feature selection techniques, and the data normalization methods used in the field. It also describes machine learning algorithms useful for recognizing emotions expressed in speech. Building upon current advances and insights gained therein, the chapter presents open challenges that remain in designing robust emotion recognition systems.

Keywords:   emotion recognition, affective computing, expressive speech, emotional databases, emotion representation, feature extraction and normalization

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