Neuroscience of Emotion and Affective Dynamics Lab, Faculty of Psychology and Educational Sciences and Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland.
PLoS One. 2022 Jun 29;17(6):e0267432. doi: 10.1371/journal.pone.0267432. eCollection 2022.
Prior research has established that valence-trustworthiness and power-dominance are the two main dimensions of voice evaluation at zero-acquaintance. These impressions shape many of our interactions and high-impact decisions, so it is crucial for many domains to understand this dynamic. Yet, the relationship between acoustical properties of novel voices and personality/attitudinal traits attributions remains poorly understood. The fundamental problem of understanding vocal impressions and relative decision-making is linked to the complex nature of the acoustical properties in voices. In order to disentangle this relationship, this study extends the line of research on the acoustical bases of vocal impressions in two ways. First, by attempting to replicate previous finding on the bi-dimensional nature of first impressions: using personality judgements and establishing a correspondence between acoustics and voice-first-impression (VFI) dimensions relative to sex (Study 1). Second (Study 2), by exploring the non-linear relationships between acoustical parameters and VFI by the means of machine learning models. In accordance with literature, a bi-dimensional projection comprising valence-trustworthiness and power-dominance evaluations is found to explain 80% of the VFI. In study 1, brighter (high center of gravity), smoother (low shimmers), and louder (high minimum intensity) voices reflected trustworthiness, while vocal roughness (harmonic to noise-ratio), energy in the high frequencies (Energy3250), pitch (Quantile 1, Quantile 5) and lower range of pitch values reflected dominance. In study 2, above chance classification of vocal profiles was achieved by both Support Vector Machine (77.78%) and Random-Forest (Out-Of-Bag = 36.14) classifiers, generally confirming that machine learning algorithms could predict first impressions from voices. Hence results support a bi-dimensional structure to VFI, emphasize the usefulness of machine learning techniques in understanding vocal impressions, and shed light on the influence of sex on VFI formation.
先前的研究已经证实，在零熟悉度的情况下，评价声音的主要维度有两个，分别是积极度-可信度和支配力-权力感。这些印象塑造了我们许多的互动和高影响力的决策，因此理解这种动态对于许多领域来说至关重要。然而，新声音的声学特性与个性/态度特征归因之间的关系仍未得到很好的理解。理解声音印象和相对决策的根本问题与声音中的复杂声学特性有关。为了理清这种关系，本研究通过两种方式扩展了对声音印象声学基础的研究。首先，通过尝试复制关于第一印象的二维性质的先前发现：使用人格判断，并建立与性别相关的声学与声音第一印象（VFI）维度之间的对应关系（研究 1）。其次（研究 2），通过机器学习模型探索声学参数与 VFI 之间的非线性关系。与文献一致，包含积极度-可信度和支配力-权力感评价的二维投影被发现可以解释 80%的 VFI。在研究 1 中，更明亮（高重心）、更平滑（低颤音）和更大声（高最小强度）的声音反映了可信度，而声音粗糙度（谐波与噪声比）、高频能量（Energy3250）、音高（Quantile 1，Quantile 5）和较低的音高值范围反映了支配力。在研究 2 中，支持向量机（77.78%）和随机森林（Out-Of-Bag = 36.14）分类器都实现了超过机会水平的声音轮廓分类，通常证实机器学习算法可以从声音预测第一印象。因此，结果支持 VFI 的二维结构，强调了机器学习技术在理解声音印象方面的有用性，并揭示了性别对 VFI 形成的影响。
