HUN-REN Hungarian Research Centre for Linguistics, Benczúr U. 33, 1068, Budapest, Hungary.
Humboldt-Universität zu Berlin, Unter den Linden 6, 10117, Berlin, Germany.
Cogn Process. 2024 Feb;25(1):89-106. doi: 10.1007/s10339-023-01168-8. Epub 2023 Nov 23.
Laughter is one of the most common non-verbal features; however, contrary to the previous assumptions, it may also act as signals of bonding, affection, emotional regulation agreement or empathy (Scott et al. Trends Cogn Sci 18:618-620, 2014). Although previous research agrees that laughter does not form a uniform group in many respects, different types of laughter have been defined differently by individual research. Due to the various definitions of laughter, as well as their different methodologies, the results of the previous examinations were often contradictory. The analysed laughs were often recorded in controlled, artificial situations; however, less is known about laughs from social conversations. Thus, the aim of the present study is to examine the acoustic realisation, as well as the automatic classification of laughter that appear in human interactions according to whether listeners consider them to be voluntary or involuntary. The study consists of three parts using a multi-method approach. Firstly, in the perception task, participants had to decide whether the given laughter seemed to be rather involuntary or voluntary. In the second part of the experiment, those sound samples of laughter were analysed that were considered to be voluntary or involuntary by at least 66.6% of listeners. In the third part, all the sound samples were grouped into the two categories by an automatic classifier. The results showed that listeners were able to distinguish laughter extracted from spontaneous conversation into two different types, as well as the distinction was possible on the basis of the automatic classification. In addition, there were significant differences in acoustic parameters between the two groups of laughter. The results of the research showed that, although the distinction between voluntary and involuntary laughter categories appears based on the analysis of everyday, spontaneous conversations in terms of the perception and acoustic features, there is often an overlap in the acoustic features of voluntary and involuntary laughter. The results will enrich our previous knowledge of laughter and help to describe and explore the diversity of non-verbal vocalisations.
笑声是最常见的非言语特征之一;然而,与之前的假设相反,它也可能作为联系、情感、情绪调节一致或同理心的信号(Scott 等人,Trends Cogn Sci 18:618-620, 2014)。尽管之前的研究都同意,在很多方面,笑声并不形成一个统一的群体,但不同类型的笑声已经被个别研究以不同的方式定义。由于笑声有各种不同的定义,以及它们不同的方法,之前的研究结果往往是相互矛盾的。分析的笑声通常是在控制的、人为的情况下录制的;然而,关于社交对话中的笑声,人们知之甚少。因此,本研究的目的是根据听众认为笑声是自愿的还是非自愿的,来检验出现在人际互动中的笑声的声学实现以及自动分类。该研究使用多方法的方法,分为三个部分。首先,在感知任务中,参与者必须判断给定的笑声是更倾向于自愿还是非自愿。在实验的第二部分,对那些至少有 66.6%的听众认为是自愿或非自愿的笑声样本进行分析。在第三部分,所有的声音样本都由一个自动分类器分为两个类别。结果表明,听众能够将从自发对话中提取的笑声分为两种不同的类型,而且这种区分是基于自动分类的。此外,两种类型的笑声在声学参数上存在显著差异。研究结果表明,尽管自愿和非自愿的笑声分类是基于日常自发对话的感知和声学特征进行的,但在自愿和非自愿笑声的声学特征之间往往存在重叠。研究结果将丰富我们之前对笑声的认识,并有助于描述和探索非言语发声的多样性。