Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Emotion in GamesEmotion Modelling via Speech Content and Prosody: In Computer Games and Elsewhere

Emotion in Games: Emotion Modelling via Speech Content and Prosody: In Computer Games and Elsewhere [The chapter describes a typical modern speech emotion recognition engine as can be used to enhance computer games’ or other technical systems’ emotional intelligence. Acquisition of human affect via the spoken content and its prosody and further acoustic features is highlighted. Features for both of these information streams are shortly discussed along chunking of the stream. Decision making with and without training data is presented, each. A particular focus is then laid on autonomous learning and adaptation methods as well as the required calculation of confidence measures. Practical aspects include the encoding of the information, distribution of the processing, and available toolkits. Benchmark performances are given by typical competitive challenges in the field.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Emotion in GamesEmotion Modelling via Speech Content and Prosody: In Computer Games and Elsewhere

Part of the Socio-Affective Computing Book Series (volume 4)
Editors: Karpouzis, Kostas; Yannakakis, Georgios N.
Schuller, Björn
Emotion in Games — Nov 4, 2016
Download PDF
17 pages

Loading next page...
 
/lp/springer-journals/emotion-in-games-emotion-modelling-via-speech-content-and-prosody-in-jbbyycnFI5
Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-41314-3
Pages
85 –102
DOI
10.1007/978-3-319-41316-7_5
Publisher site
See Chapter on Publisher Site

Abstract

[The chapter describes a typical modern speech emotion recognition engine as can be used to enhance computer games’ or other technical systems’ emotional intelligence. Acquisition of human affect via the spoken content and its prosody and further acoustic features is highlighted. Features for both of these information streams are shortly discussed along chunking of the stream. Decision making with and without training data is presented, each. A particular focus is then laid on autonomous learning and adaptation methods as well as the required calculation of confidence measures. Practical aspects include the encoding of the information, distribution of the processing, and available toolkits. Benchmark performances are given by typical competitive challenges in the field.]

Published: Nov 4, 2016

Keywords: Speech Signal; Emotion Recognition; Acoustic Feature; Independent Component Analysis; Automatic Speech Recognition

There are no references for this article.