Research on prominence perception has made use of animated agents and controlled speech in experimental settings, but these methodologies have disregarded some aspects of the acoustic and visual correlates of prominence. To overcome these limitations we propose a new methodological approach using spontaneous speech data. For this, we created a small database with extracts from a television talent show and neutralised the prominence-lending properties of the acoustic cues of prominence in the speech signal. In our pilot study twelve naïve listeners marked words for binary prominence (prominent vs. non-prominent) in two modalities, i.e. audio-only and audiovisual, under three conditions involving neutralisation of (a) fundamental frequency, (b) intensity, and (c) both fundamental frequency and intensity. Additionally, the marks of two trained listeners served as control condition. Different generalised linear mixed models were estimated and compared using the Akaike Information Criterion (AIC). The most parsimonious model was then examined using traditional null-hypothesis testing in order to provisionally establish the effects of our independent variables on prominence marking. We argue that spontaneous speech can be successfully applied to the study of the multimodal perception of prominence.

关于突出感知的研究已经在实验环境中使用了动画代理和受控语音，但是这些方法忽略了突出的声学和视觉相关性的某些方面。为了克服这些限制，我们提出了一种使用自发语音数据的新方法论方法。为此，我们创建了一个小型数据库，其中包含电视节目选秀节目的摘录，并消除了语音信号中声音突出提示的突出性。在我们的初步研究中，十二个纯朴的听众在三种条件下将（a）基本频率，（b）强度和（ c）基本频率和强度。另外，两名受过训练的听众的成绩作为控制条件。使用Akaike信息准则（AIC）估算并比较了不同的广义线性混合模型。然后使用传统的零假设检验检查最简约的模型，以临时确定我们独立变量对突出标记的影响。我们认为，自发的言语可以成功地应用于对突显的多峰感知的研究。