Abstract
This paper describes some of the acoustic characteristics that influence peoples’ judgments about others. The database used was the multilingual corpus recorded with speakers in communicative dialogue contexts (e.g., Rilliard et al. 2013). The acoustic measurements were F0, intensity, HNR, H1-H2, and formant frequencies (F1, F2, and F3). The personality assessment was based on that proposed by Costa and McCrae (1992). A Multiple Factor Analysis (MFA) related the acoustic measures, the performance scores for each attitude, and the number of high, high-medium, low-medium and low ratings in the 5 personality traits, for audio-only and for audio-visual modalities. The results show that the most expressive speakers, those who produced the widest range in acoustic changes, were perceived as more EXTROVERTED and CONSCIENTIOUS. Speakers with high noise levels in the voice were judged with low AGREEABLENESS, and produced the best expressions involving an imposition on the interlocutor. Speakers judged as having high NEUROTICISM and low OPENNESS were perceived as the best performers for expressions with strong social constraints.
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Acknowledgements
The authors are deeply indebted to the speakers and listeners from Waseda, Bordeaux, New Mexico, and Black Hills State Universities who participated in these experiments. Also, we acknowledge the French ANR) PADE and SEDUCTION CPU (ANR-10-IDEX-03-02) grants, the Japan Society for the Promotion of Science (JSPS), Grants-in-Aid for Scientific Research (A) #25240026 to the first author, and the Brazilian CNPq agency (bolsa PV 406177/2015-5) to the second author. Part of the contents of this paper appeared in Madureira, S. (Ed.) Sonoridades - Sonorities. 1. São Paulo: Edição da Pontifícia Universidade Católica de São Paulo, 2016.
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Erickson, D., Rilliard, A., de Moraes, J., Shochi, T. (2018). Personality Judgments Based on Speaker’s Social Affective Expressions. In: Fang, Q., Dang, J., Perrier, P., Wei, J., Wang, L., Yan, N. (eds) Studies on Speech Production. ISSP 2017. Lecture Notes in Computer Science(), vol 10733. Springer, Cham. https://doi.org/10.1007/978-3-030-00126-1_1
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