Matching Unfamiliar Voices to Static and Dynamic Faces: No Evidence for a Dynamic Face Advantage in a Simultaneous Presentation Paradigm

The Benefit of Bimodal Training in Voice Learning

It is known that talkers can be recognized by listening to their specific vocal qualities-breathiness and fundamental frequencies. However, talker identification can also occur by focusing on the talkers' unique articulatory style, which is known to be available auditorily and visually and can be shared across modalities. Evidence shows that voices heard while seeing talkers' faces are later recognized better on their own compared to the voices heard alone. The present study investigated whether the facilitation of voice learning through facial cues relies on talker-specific articulatory or nonarticulatory facial information. Participants were initially trained to learn the voices of ten talkers presented either on their own or together with (a) an articulating face, (b) a static face, or (c) an isolated articulating mouth. Participants were then tested on recognizing the voices on their own regardless of their training modality. Consistent with previous research, voices learned with articulating faces were recognized better on their own compared to voices learned alone. However, isolated articulating mouths did not provide an advantage in learning the voices. The results demonstrated that learning voices while seeing faces resulted in better voice learning compared to the voices learned alone.

Unimodal and cross-modal identity judgements using an audio-visual sorting task: Evidence for independent processing of faces and voices

Unimodal and cross-modal information provided by faces and voices contribute to identity percepts. To examine how these sources of information interact, we devised a novel audio-visual sorting task in which participants were required to group video-only and audio-only clips into two identities. In a series of three experiments, we show that unimodal face and voice sorting were more accurate than cross-modal sorting: While face sorting was consistently most accurate followed by voice sorting, cross-modal sorting was at chancel level or below. In Experiment 1, we compared performance in our novel audio-visual sorting task to a traditional identity matching task, showing that unimodal and cross-modal identity perception were overall moderately more accurate than the traditional identity matching task. In Experiment 2, separating unimodal from cross-modal sorting led to small improvements in accuracy for unimodal sorting, but no change in cross-modal sorting performance. In Experiment 3, we explored the effect of minimal audio-visual training: Participants were shown a clip of the two identities in conversation prior to completing the sorting task. This led to small, nonsignificant improvements in accuracy for unimodal and cross-modal sorting. Our results indicate that unfamiliar face and voice perception operate relatively independently with no evidence of mutual benefit, suggesting that extracting reliable cross-modal identity information is challenging.

Explaining face-voice matching decisions: The contribution of mouth movements, stimulus effects and response biases

Previous studies have shown that face-voice matching accuracy is more consistently above chance for dynamic (i.e. speaking) faces than for static faces. This suggests that dynamic information can play an important role in informing matching decisions. We initially asked whether this advantage for dynamic stimuli is due to shared information across modalities that is encoded in articulatory mouth movements. Participants completed a sequential face-voice matching task with (1) static images of faces, (2) dynamic videos of faces, (3) dynamic videos where only the mouth was visible, and (4) dynamic videos where the mouth was occluded, in a well-controlled stimulus set. Surprisingly, after accounting for random variation in the data due to design choices, accuracy for all four conditions was at chance. Crucially, however, exploratory analyses revealed that participants were not responding randomly, with different patterns of response biases being apparent for different conditions. Our findings suggest that face-voice identity matching may not be possible with above-chance accuracy but that analyses of response biases can shed light upon how people attempt face-voice matching. We discuss these findings with reference to the differential functional roles for faces and voices recently proposed for multimodal person perception.