Faces and voices each convey multiple cues enabling us to tell people apart. Research on face and voice distinctiveness commonly utilizes multidimensional space to represent these complex, perceptual abilities. We extend this framework to examine how a combined face-voice space would relate to its constituent face and voice spaces. Participants rated videos of speakers for their dissimilarity in face only, voice only, and face-voice together conditions. Multiple dimensional scaling (MDS) and regression analyses showed that whereas face-voice space more closely resembled face space, indicating visual dominance, face-voice distinctiveness was best characterized by a multiplicative integration of face-only and voice-only distinctiveness, indicating that auditory and visual cues are used interactively in person-distinctiveness judgments. Further, the multiplicative integration could not be explained by the small correlation found between face-only and voice-only distinctiveness. As an exploratory analysis, we next identified auditory and visual features that correlated with the dimensions in the MDS solutions. Features pertaining to facial width, lip movement, spectral centroid, fundamental frequency, and loudness variation were identified as important features in face-voice space. We discuss the implications of our findings in terms of person perception, recognition, and face-voice matching abilities.

面孔和声音都传达了多种线索，使我们能够区分人。对面部和声音的独特性的研究通常利用多维空间来表示这些复杂的感知能力。我们扩展了该框架，以检查组合的面部语音空间如何与其组成的面部和语音空间相关。与会者对演讲者的视频进行了评分，原因是它们在仅面部，仅语音和面部声音共同条件下的差异。多维标度（MDS）和回归分析表明，尽管脸部语音空间与脸部空间更相似，表明视觉支配力，但脸部语音独特性的最佳体现是仅面部表情和仅语音独特性的乘法整合，表明听觉和视觉提示在人与人之间的判断中交互使用。进一步，仅面部识别和语音识别之间的较小相关性无法解释乘法积分。作为探索性分析，我们接下来确定与MDS解决方案中的尺寸相关的听觉和视觉特征。与面部宽度，嘴唇运动，频谱重心，基频和响度变化有关的特征被确定为面部语音空间中的重要特征。我们从人的知觉，识别和面部声音匹配能力方面讨论研究结果的含义。与面部宽度，嘴唇运动，频谱重心，基频和响度变化有关的特征被确定为面部语音空间中的重要特征。我们从人的知觉，识别和面部声音匹配能力方面讨论研究结果的含义。与面部宽度，嘴唇运动，频谱重心，基本频率和响度变化有关的特征被确定为面部语音空间中的重要特征。我们从人的知觉，识别和面部声音匹配能力方面讨论研究结果的含义。