Abstract
Nowadays affective computing is one of the most interesting and challenging research areas among the human computer interaction (HCI) researchers. One of the potential applications of emotion detection is to analyze confidence level of a speaker. In human-computer or human-human interaction systems, speech based confidence level check can provide users with improved services. Confidence level checking would be useful in various applications e.g. job recruitment process. In this paper a solution to similar kind of applications is proposed to differentiate between a confident person and non-confident person. A set of speech features have been selected empirically to support the concept that speech conveys not only the linguistic messages, but also emotional content. A protruding result has been sought on using SVM and KNN classifiers in determining confidence level of a human. The accuracy and efficiency of result is found good and reliable.
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Malviya, S., Singh, V.K., Umrao, J. (2016). Discriminating Confident and Non-confidant Speaker Based on Acoustic Speech Features. In: Choudhary, R., Mandal, J., Auluck, N., Nagarajaram, H. (eds) Advanced Computing and Communication Technologies. Advances in Intelligent Systems and Computing, vol 452. Springer, Singapore. https://doi.org/10.1007/978-981-10-1023-1_18
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DOI: https://doi.org/10.1007/978-981-10-1023-1_18
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