Speech based automatic personality perception using spectral features

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• 作者：S. Jothilakshmi ; J. Sangeetha ; R. Brindha
• 关键词：Personality traits ; Automatic personality perception ; Frequency domain linear prediction ; Mel frequency cepstral coefficients
• 刊名：International Journal of Speech Technology
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：20
• 期：1
• 页码：43-50
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing; Social Sciences, general; Artificial Intelligence (incl. Robotics);
• 出版者：Springer US
• ISSN：1572-8110
• 卷排序：20

文摘

Automatic personality perception is the prediction of personality that others attribute to a person in a given situation. The aim of automatic personality perception is to forecast the behaviour of the speaker perceived by the listener from nonverbal behavior. Extroversion, Conscientiousness, Agreeableness, Neuroticism, and Openness are the speaker traits used for personality assessment. In this work, a speaker trait prediction approach for automatic personality assessment is proposed. This approach is based on modeling the relationship between speech signal and personality traits using spectral features. The experiments are achieved over the SSPNet Personality Corpus. The Frequency Domain Linear Prediction and Mel Frequency Cepstral Coefficient features are extracted for the prediction of speaker traits. The classification is done using Instance based k-Nearest neighbor and Support Vector Machine (SVM) classifiers. The experimental results show that k-Nearest Neighbor classifier outperforms SVM classifier. The classification accuracy is between 90 and 100%.