Abstract
In this work, we perform feature selection on fifty-six prosodic features (such as intensity, formants, pause) extracted from the MIT-interview dataset. These features help in rating the various personality traits (Engaged, Excited, Friendly, Speaking Rate) which in turn help to determine an interviewee performance. First, we have demonstrated how the top few prosodic features are selected based upon the mutual information gain that gives better performance for rating personality trait rather than taking all fifty-six prosodic features. Second, a set of experiments has been conducted under various regression models of different parameter settings, and their performances are compared. At last, we have listed top selected prosodic features for each personality trait that give better results compared to total fifty-six prosodic features. It is found out that for rating personality trait such as ‘Engaged’ and ‘Excited’, prosodic features related to intensity and pitch plays a major role while for ‘Friendly’, prosodic features such as maximum-duration of pause and percentage-breaks, take a major role apart from intensity and pitch. Similarly, for rating personality trait ‘Speaking Rate’, prosodic features computed related to first formant, third formant, total pause duration, percentage-breaks are more relevant.
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Mishra, R., Barnwal, S.K., Malviya, S., Mishra, P., Tiwary, U.S. (2020). Prosodic Feature Selection of Personality Traits for Job Interview Performance. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_63
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