Feature Optimization of Speech Emotion Recognition - Journal of Biomedical Science and Engineering

JBiSE > Vol.9 No.10, September 2016

Journal of Biomedical Science and Engineering

Volume 9, Issue 10 (September 2016)

ISSN Print: 1937-6871 ISSN Online: 1937-688X

Google-based Impact Factor: 0.66 Citations h5-index & Ranking

Feature Optimization of Speech Emotion Recognition ()

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DOI: 10.4236/jbise.2016.910B005 1,749 Downloads 3,834 Views Citations

Author(s)

Chunxia Yu, Ling Xie, Weiping Hu^*

Affiliation(s)

GuangXi Key Lab of Multi-Source Information Mining and Security, GuangXi Normal University, Guilin, China.

ABSTRACT

Speech emotion is divided into four categories, Fear, Happy, Neutral and Surprise in this paper. Traditional features and their statistics are generally applied to recognize speech emotion. In order to quantify each feature’s contribution to emotion recogni-tion, a method based on the Back Propagation (BP) neural network is adopted. Then we can obtain the optimal subset of the features. What’s more, two new characteristics of speech emotion, MFCC feature extracted from the fundamental frequency curve (MFCCF0) and amplitude perturbation parameters extracted from the short- time av-erage magnitude curve (APSAM), are added to the selected features. With the Gaus-sian Mixture Model (GMM), we get the highest average recognition rate of the four emotions 82.25%, and the recognition rate of Neutral 90%.

KEYWORDS

Speech Emotion Recognition, Feature Selection, Feature Extraction, BP Neural Network, GMM

Share and Cite:

Yu, C. , Xie, L. and Hu, W. (2016) Feature Optimization of Speech Emotion Recognition. Journal of Biomedical Science and Engineering, 9, 37-43. doi: 10.4236/jbise.2016.910B005.

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[1]	Speech Emotion Recognition Using MFCC and Wide Residual Network
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[2]	Emotional Speech Recognition Using Particle Swarm Optimization Algorithm
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[3]	Child emotion recognition using probabilistic neural network with effective features
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[4]	Notice of Removal: Speech Emotion Recognition Based on Hyper-Prosodic Features
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[5]	Speech Emotion Recognition Based on Improved MFCC
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[6]	Emotional Speech Recognition using Optimized
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[7]	Speech Emotion Recognition Based on Hyper-Prosodic Features
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