International Journal of Speech Technology

, Volume 20, Issue 1, pp 43–50 | Cite as

Speech based automatic personality perception using spectral features



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%.


Personality traits Automatic personality perception Frequency domain linear prediction Mel frequency cepstral coefficients 


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAnnamalai UniversityAnnamalainagarIndia
  2. 2.Department of Computer Science and EngineeringSastra UniversityThanjavurIndia
  3. 3.Department of Computer Science and EngineeringSRM UniversityKattankulathurIndia

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