When voices get emotional: A corpus of nonverbal vocalizations for research on emotion processing

Abstract

Nonverbal vocal expressions, such as laughter, sobbing, and screams, are an important source of emotional information in social interactions. However, the investigation of how we process these vocal cues entered the research agenda only recently. Here, we introduce a new corpus of nonverbal vocalizations, which we recorded and submitted to perceptual and acoustic validation. It consists of 121 sounds expressing four positive emotions (achievement/triumph, amusement, sensual pleasure, and relief) and four negative ones (anger, disgust, fear, and sadness), produced by two female and two male speakers. For perceptual validation, a forced choice task was used (n = 20), and ratings were collected for the eight emotions, valence, arousal, and authenticity (n = 20). We provide these data, detailed for each vocalization, for use by the research community. High recognition accuracy was found for all emotions (86%, on average), and the sounds were reliably rated as communicating the intended expressions. The vocalizations were measured for acoustic cues related to temporal aspects, intensity, fundamental frequency (f0), and voice quality. These cues alone provide sufficient information to discriminate between emotion categories, as indicated by statistical classification procedures; they are also predictors of listeners' emotion ratings, as indicated by multiple regression analyses. This set of stimuli seems a valuable addition to currently available expression corpora for research on emotion processing. It is suitable for behavioral and neuroscience research and might as well be used in clinical settings for the assessment of neurological and psychiatric patients. The corpus can be downloaded from Supplementary Materials.