Document Type : Research Paper
Authors
Department of Electrical and Computer Engineering, Faculty of K. N. Toosi University of Technology, Tehran, Iran.
Abstract
The personality in the present world plays a critical role in social interactions, the use of modern technologies, and individuals' success. Therefore, in the last two decades, the study of Automatic Personality Perception (APP) and Automatic Personality Recognition (APR) has become more prevalent than speech processing. These studies have shown that personality traits affect acoustic features. However, the intrinsic imbalanced distribution of personality classes across the dataset is an issue mentioned in most previous studies and the classification results suffer from it. In this paper, an innovative supervised k-fold Cross-Validation (CV) method was proposed to cope with the problem of affecting the imbalanced distribution of data across different classes. The classification outcomes showed better performance in comparison with three traditional data balancing methods. Moreover, the obtained results of the proposed evaluation method indicated that the proposed method acts as a k-fold CV method if the data distribution is balanced; otherwise, it will improve the classification results.
Keywords
Main Subjects
- Niebuhr, O. (2021). Relations between speech sciences and industry. Retrieved from http://www.letras.ufmg.br/padrao_cms/index.php?web=lbass&lang=1&page=3732&menu=&tipo=1
- Schultz, D. P., & Schultz, S. E. (2016). Theories of personality. Cengage Learning.
- Hagh-Shenas, H. (2017). Big 5 personality. Ravansanji. (In Persian). https://www.gisoom.com/book/11287274/
- Phan, L. V., & Rauthmann, J. F. (2021). Personality computing: new frontiers in personality assessment. Social and personality psychology compass, 15(7), e12624. https://doi.org/10.1111/spc3.12624
- Komarraju, M., Karau, S. J., Schmeck, R. R., & Avdic, A. (2011). The Big Five personality traits, learning styles, and academic achievement. Personality and individual differences, 51(4), 472-477.
- Mohammadi, G., & Vinciarelli, A. (2015, September). Automatic personality perception: Prediction of trait attribution based on prosodic features extended abstract. 2015 international conference on affective computing and intelligent interaction (ACII)(pp. 484-490). IEEE.
- Ponce-López, V., Chen, B., Oliu, M., Corneanu, C., Clapés, A., Guyon, I., ... & Escalera, S. (2016, October). Chalearn lap 2016: first round challenge on first impressions-dataset and results. European conference on computer vision(pp. 400-418). Springer, Cham.
- Rosenberg, A. (2018, June). Speech, prosody, and machines: nine challenges for prosody research. 9th international conference on speech prosody (pp. 784-793). Speech Prosody. DOI: 21437/SPEECHPROSODY.2018-159
- Junior, J. C. S. J., Güçlütürk, Y., Pérez, M., Güçlü, U., Andujar, C., Baró, X., ... & Escalera, S. (2019). First impressions: a survey on vision-based apparent personality trait analysis. IEEE transactions on affective computing, 13(1), 75-95. DOI: 1109/TAFFC.2019.2930058
- Vinciarelli, A., & Mohammadi, G. (2011). Towards a technology of nonverbal communication: vocal behavior in social and affective phenomena. In Affective computing and interaction: psychological, cognitive and neuroscientific perspectives(pp. 133-156). IGI Global.
- An, G., & Levitan, R. (2018, February). Lexical and acoustic deep learning model for personality recognition. In Interspeech(pp. 1761-1765). https://www.isca-speech.org/archive_v0/Interspeech_2018/pdfs/2263.pdf
- Zhao, S., Xu, Z., Liu, L., Guo, M., & Yun, J. (2018). Towards accurate deceptive opinions detection based on word order-preserving CNN. Mathematical problems in engineering, 2018. https://doi.org/10.1155/2018/2410206
- Ewen, R. B. (2014). An introduction to theories of personality. Psychology Press.
- Ghosh, A., Hossain, A. A., & Raju, S. T. U. (2021, March). Classification of diabetic retinopathy using few-shot transfer learning from imbalanced data. 7th international conference on advanced computing and communication systems (ICACCS)(Vol. 1, pp. 78-83). IEEE.
- Leevy, J. L., Khoshgoftaar, T. M., Bauder, R. A., & Seliya, N. (2018). A survey on addressing high-class imbalance in big data. Journal of big data, 5(1), 1-30.
- Fernández, A., Garcia, S., Herrera, F., & Chawla, N. V. (2018). SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. Journal of artificial intelligence research, 61, 863-905.
- Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of big data, 6(1), 1-54.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
- Yan, Y., Chen, M., Shyu, M. L., & Chen, S. C. (2015, December). Deep learning for imbalanced multimedia data classification. IEEE international symposium on multimedia (ISM)(pp. 483-488). IEEE.
- Tan, H. H., & Lim, K. H. (2019, June). Vanishing gradient mitigation with deep learning neural network optimization. 7th international conference on smart computing & communications (ICSCC)(pp. 1-4). IEEE.
- Chen, Z., & Lin, T. (2017). Automatic personality identification using writing behaviours: an exploratory study. Behaviour & information technology, 36(8), 839-845.
- Zarnoufi, R., & Abik, M. (2019, November). Big five personality traits and ensemble machine learning to detect cyber-violence in social media. International conference Europe Middle East & North Africa information systems and technologies to support learning(pp. 194-202). Springer, Cham.
- Khan, A. S., Ahmad, H., Asghar, M. Z., Saddozai, F. K., Arif, A., & Khalid, H. A. (2020). Personality classification from online text using machine learning approach. International journal of advanced computer science and applications, 11(3), 460-476.
- Horvath, A., Dras, M., Lai, C. C., & Boag, S. (2021). Predicting suicidal behavior without asking about suicidal ideation: machine learning and the role of borderline personality disorder criteria. Suicide and life‐threatening behavior, 51(3), 455-466.
- Aylett, M. P., Vazquez-Alvarez, Y., & Butkute, S. (2020, March). Creating robot personality: effects of mixing speech and semantic free utterances. Companion of the 2020 ACM/IEEE international conference on human-robot interaction(pp. 110-112). Association for Computing Machinery, New York, NY, United States. https://doi.org/10.1145/3371382.3378330
- Maillard, P., Pellaton, J., & Kramer, U. (2019). Treating comorbid depression and avoidant personality disorder: the case of Andy. Journal of clinical psychology, 75(5), 886-897.
- Pohjalainen, J., Kadioglu, S., & Räsänen, O. (2012). Feature selection for speaker traits. Thirteenth annual conference of the international speech communication association (pp. 270-273). ISCA Archive. https://www.isca-speech.org/archive_v0/archive_papers/interspeech_2012/i12_0270.pdf
- Fayet, C., Delhay, A., Lolive, D., & Marteau, P. F. (2017, August). Big Five vs. Prosodic Features as Cues to Detect Abnormality in SSPNET-Personality Corpus. In Interspeech(pp. 3281-3285). Stockholm, Sweden. https://www.isca-speech.org/archive_v0/Interspeech_2017/pdfs/1194.PDF
- Schuller, B., Steidl, S. and Batliner, A. (2009). The interspeech 2009 emotion challenge ( 211486). Retrieved from https://d-nb.info/1218972491/34
- An, G., Levitan, S. I., Levitan, R., Rosenberg, A., Levine, M., & Hirschberg, J. (2016). Automatically Classifying Self-Rated Personality Scores from Speech. In Interspeech(pp. 1412-1416). San Francisco, USA. https://www.isca-speech.org/archive_v0/Interspeech_2016/pdfs/1328.PDF
- Jothilakshmi, S., Sangeetha, J., & Brindha, R. (2017). Speech based automatic personality perception using spectral features. International journal of speech technology, 20(1), 43-50.
- Mohammadi, G., Origlia, A., Filippone, M., & Vinciarelli, A. (2012, October). From speech to personality: Mapping voice quality and intonation into personality differences. Proceedings of the 20th ACM international conference on Multimedia(pp. 789-792). Association for Computing Machinery, New York, NY, United States. https://doi.org/10.1145/2393347.2396313
- Koutsombogera, M., Sarthy, P., & Vogel, C. (2020, September). Acoustic features in dialogue dominate accurate personality trait classification. IEEE international conference on human-machine systems (ICHMS)(pp. 1-3). IEEE.
- Kampman, O., Siddique, F. B., Yang, Y., & Fung, P. (2019). Adapting a virtual agent to user personality. In Advanced Social Interaction with Agents(pp. 111-118). Springer, Cham.
- Liu, Z. T., Rehman, A., Wu, M., Cao, W. H., & Hao, M. (2020). Speech personality recognition based on annotation classification using log-likelihood distance and extraction of essential audio features. IEEE transactions on multimedia, 23, 3414-3426.
- Diener, E. and Lucas, R.E. (2019). Personality traits. Retrieved from https://nobaproject.com/modules/personality-traits
- Mohammadi, G., Vinciarelli, A., & Mortillaro, M. (2010, October). The voice of personality: mapping nonverbal vocal behavior into trait attributions. Proceedings of the 2nd international workshop on Social signal processing(pp. 17-20). Association for Computing Machinery, New York, NY, United States. https://doi.org/10.1145/1878116.1878123
- Fallahnezhad, M., Vali, M., & Khalili, M. (2017, May). Automatic Personality Recognition from reading text speech. In 2017 Iranian Conference on Electrical Engineering (ICEE)(pp. 18-23). IEEE.
- Zaferani, E. J., Teshnehlab, M., & Vali, M. (2021). Automatic personality traits perception using asymmetric auto-encoder. IEEE access, 9, 68595-68608.
- Le, Q. V. (2015). A tutorial on deep learning part 2: autoencoders, convolutional neural networks and recurrent neural networks. Google brain, 20, 1-20.
- Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics, 19(6), 1236-1246.
- Balasuriya, J., & Yang, Y. (2019). The role of personality traits in pension decisions: findings and policy recommendations. Applied economics, 51(27), 2901-2920.
- Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017). Sentiment analysis is a big suitcase. IEEE intelligent systems, 32(6), 74-80. https://sentic.net/sentiment-analysis-suitcase.pdf
- Jung, Y. (2018). Multiple predicting K-fold cross-validation for model selection. Journal of nonparametric statistics, 30(1), 197-215.
- Mehta, Y., Majumder, N., Gelbukh, A., & Cambria, E. (2020). Recent trends in deep learning based personality detection. Artificial intelligence review, 53(4), 2313-2339.
- Schuller, B., Steidl, S., Batliner, A., Nöth, E., Vinciarelli, A., Burkhardt, F., ... & Weiss, B. (2015). A survey on perceived speaker traits: Personality, likability, pathology, and the first challenge. Computer speech & language, 29(1), 100-131.
)