Discriminating Emotions in the Valence Dimension from Speech Using Timbre Features

Discriminating Emotions in the Valence Dimension from Speech Using Timbre Features

The most used and well-known acoustic features of a speech signal, the Mel frequency cepstral coefficients (MFCC), cannot characterize emotions in speech sufficiently when a classification is performed to classify both discrete emotions (i.e., anger, happiness, sadness, and neutral) and emotions in...

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Bibliographic Details
Main Authors: Anvarjon Tursunov, Soonil Kwon, Hee-Suk Pang
Format: Article
Language:English
Published: MDPI AG 2019-06-01
Series:Applied Sciences
Subjects:
timbre acoustic features
valence dimension
affective computing
emotion recognition
neural networks
speech processing
Online Access:https://www.mdpi.com/2076-3417/9/12/2470

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https://www.mdpi.com/2076-3417/9/12/2470

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