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DOI: 10.14569/IJACSA.2020.0110679
PDF

Automated Recognition of Sincere Apologies from Acoustics of Speech

Author 1: Zafi Sherhan Syed
Author 2: Muhammad Shehram Shah
Author 3: Abbas Shah Syed

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 6, 2020.

  • Abstract and Keywords
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Abstract: Sincerity is an important characteristic of communicative behavior which represents an honest, truthful, and genuine display of verbal and non-verbal expressions. Individuals who are deemed sincere often appear more charismatic and can influence a large number of people. In this paper, we propose a multi-model fusion framework to identify sincerely delivered apologies by modelling difference between acoustics of sincere and insincere utterances. The efficacy of this framework is benchmarked using the Sincere Apology Corpus (SAC). We show that our proposed methods can improve the baseline classification performance (in terms of unweighted average recall) for SAC from 66.02% to 70.97% for the validation partition and 66.61% to 75.49% for the test partition. Moreover, as part of our investigation, we found that gender dependency can influence the classification performance of machine learning models, with models trained for male subjects performing better than those trained for female subjects.

Keywords: Sincerity; affective computing; social signal processing

Zafi Sherhan Syed, Muhammad Shehram Shah and Abbas Shah Syed, “Automated Recognition of Sincere Apologies from Acoustics of Speech” International Journal of Advanced Computer Science and Applications(IJACSA), 11(6), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110679

@article{Syed2020,
title = {Automated Recognition of Sincere Apologies from Acoustics of Speech},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110679},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110679},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {6},
author = {Zafi Sherhan Syed and Muhammad Shehram Shah and Abbas Shah Syed}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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