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在较大规模的群体中互动时,说话者会更具合作性,更少个人主义。

Speakers are more cooperative and less individual when interacting in larger group sizes.

作者信息

Pellegrino Elisa, Dellwo Volker

机构信息

Department of Computational Linguistics, University of Zurich, Zurich, Switzerland.

出版信息

Front Psychol. 2023 Jun 5;14:1145572. doi: 10.3389/fpsyg.2023.1145572. eCollection 2023.

Abstract

INTRODUCTION

Cooperation, acoustically signaled through vocal convergence, is facilitated when group members are more similar. Excessive vocal convergence may, however, weaken individual recognizability. This study aimed to explore whether constraints to convergence can arise in circumstances where interlocutors need to enhance their vocal individuality. Therefore, we tested the effects of group size (3 and 5 interactants) on vocal convergence and individualization in a social communication scenario in which individual recognition by voice is at stake.

METHODS

In an interactive game, players had to recognize each other through their voices while solving a cooperative task online. The vocal similarity was quantified through similarities in speaker i-vectors obtained through probabilistic linear discriminant analysis (PLDA). Speaker recognition performance was measured through the system Equal Error Rate (EER).

RESULTS

Vocal similarity between-speakers increased with a larger group size which indicates a higher cooperative vocal behavior. At the same time, there was an increase in EER for the same speakers between the smaller and the larger group size, meaning a decrease in overall recognition performance.

DISCUSSION

The decrease in vocal individualization in the larger group size suggests that ingroup cooperation and social cohesion conveyed through acoustic convergence have priority over individualization in larger groups of unacquainted speakers.

摘要

引言

当群体成员更加相似时,通过声音趋同进行声学信号传递的合作会更容易实现。然而,过度的声音趋同可能会削弱个体的可识别性。本研究旨在探讨在对话者需要增强其声音个性的情况下,是否会出现对趋同的限制。因此,我们在一个声音个体识别至关重要的社交沟通场景中,测试了群体规模(3人和5人互动者)对声音趋同和个性化的影响。

方法

在一个互动游戏中,玩家在在线解决合作任务时必须通过声音相互识别。声音相似度通过概率线性判别分析(PLDA)获得的说话者i-向量的相似度进行量化。说话者识别性能通过系统等错误率(EER)来衡量。

结果

说话者之间的声音相似度随着群体规模的增大而增加,这表明更高的合作性声音行为。同时,对于相同的说话者,较小群体规模和较大群体规模之间的EER有所增加,这意味着整体识别性能下降。

讨论

较大群体规模中声音个性化的降低表明,在不熟悉的说话者组成的较大群体中,通过声学趋同传达的群体内合作和社会凝聚力优先于个性化。

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