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Emotion Analysis of Different Age Groups From Voice Using Machine Learning Approach

Emotion Analysis of Different Age Groups From Voice Using Machine Learning Approach

Mihir Narayan Mohanty
Copyright: © 2020 |Pages: 22
ISBN13: 9781799810216|ISBN10: 1799810216|ISBN13 Softcover: 9781799810223|EISBN13: 9781799810230
DOI: 10.4018/978-1-7998-1021-6.ch008
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MLA

Mohanty, Mihir Narayan. "Emotion Analysis of Different Age Groups From Voice Using Machine Learning Approach." Critical Approaches to Information Retrieval Research, edited by Muhammad Sarfraz, IGI Global, 2020, pp. 150-171. https://doi.org/10.4018/978-1-7998-1021-6.ch008

APA

Mohanty, M. N. (2020). Emotion Analysis of Different Age Groups From Voice Using Machine Learning Approach. In M. Sarfraz (Ed.), Critical Approaches to Information Retrieval Research (pp. 150-171). IGI Global. https://doi.org/10.4018/978-1-7998-1021-6.ch008

Chicago

Mohanty, Mihir Narayan. "Emotion Analysis of Different Age Groups From Voice Using Machine Learning Approach." In Critical Approaches to Information Retrieval Research, edited by Muhammad Sarfraz, 150-171. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1021-6.ch008

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Abstract

Emotion detection from voice is a complex task, whereas from the facial expression it is easy. In this chapter, an attempt is taken to detect the emotion through machine using neural network-based models and compared. As no complete database is available for different age groups, a small database is generated. To know the emotion of different age groups substantially, three groups have been generated with each group of 20 subjects. The efficient prosodic features are considered initially. Further, the combination of those features are taken. Each set of features are fed to the models for classification and detection. Angry, happy, and sad are the three emotions verified for different group of persons. It is found that the classifier provides 96% of accuracy. In earlier work, cluster-based techniques with simple features pitch, speech rate, and log energy were verified. As an extension, the combination of features along with machine learning model is verified in this work.

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