Publications

Abstract

Spoken words are highly variable and therefore listeners interpret speech sounds relative to the surrounding acoustic context, such as the speech rate of a preceding sentence. For instance, a vowel midway between short /ɑ/ and long /a:/ in Dutch is perceived as short /ɑ/ in the context of preceding slow speech, but as long /a:/ if preceded by a fast context. Despite the well-established influence of visual articulatory cues on speech comprehension, it remains unclear whether visual cues to speech rate also influence subsequent spoken word recognition. In two ‘Go Fish’-like experiments, participants were presented with audio-only (auditory speech + fixation cross), visual-only (mute videos of talking head), and audiovisual (speech + videos) context sentences, followed by ambiguous target words containing vowels midway between short /ɑ/ and long /a:/. In Experiment 1, target words were always presented auditorily, without visual articulatory cues. Although the audio-only and audiovisual contexts induced a rate effect (i.e., more long /a:/ responses after fast contexts), the visual-only condition did not. When, in Experiment 2, target words were presented audiovisually, rate effects were observed in all three conditions, including visual-only. This suggests that visual cues to speech rate in a context sentence influence the perception of following visual target cues (e.g., duration of lip aperture), which at an audiovisual integration stage bias participants’ target categorization responses. These findings contribute to a better understanding of how what we see influences what we hear.

Abstract

Face-to-face communication is multimodal at its core: it consists of a combination of vocal and visual signalling. However, current evidence suggests that, in the absence of an established communication system, visual signalling, especially in the form of visible gesture, is a more powerful form of communication than vocalisation, and therefore likely to have played a primary role in the emergence of human language. This argument is based on experimental evidence of how vocal and visual modalities (i.e., gesture) are employed to communicate about familiar concepts when participants cannot use their existing languages. To investigate this further, we introduce an experiment where pairs of participants performed a referential communication task in which they described unfamiliar stimuli in order to reduce reliance on conventional signals. Visual and auditory stimuli were described in three conditions: using visible gestures only, using non-linguistic vocalisations only and given the option to use both (multimodal communication). The results suggest that even in the absence of conventional signals, gesture is a more powerful mode of communication compared to vocalisation, but that there are also advantages to multimodality compared to using gesture alone. Participants with an option to produce multimodal signals had comparable accuracy to those using only gesture, but gained an efficiency advantage. The analysis of the interactions between participants showed that interactants developed novel communication systems for unfamiliar stimuli by deploying different modalities flexibly to suit their needs and by taking advantage of multimodality when required.

Abstract

In human face-to-face communication, speech is frequently accompanied by visual signals, especially communicative hand gestures. Analyzing these visual signals requires detailed manual annotation of video data, which is often a labor-intensive and time-consuming process. To facilitate this process, we here present SPUDNIG (SPeeding Up the Detection of Non-iconic and Iconic Gestures), a tool to automatize the detection and annotation of hand movements in video data. We provide a detailed description of how SPUDNIG detects hand movement initiation and termination, as well as open-source code and a short tutorial on an easy-to-use graphical user interface (GUI) of our tool. We then provide a proof-of-principle and validation of our method by comparing SPUDNIG’s output to manual annotations of gestures by a human coder. While the tool does not entirely eliminate the need of a human coder (e.g., for false positives detection), our results demonstrate that SPUDNIG can detect both iconic and non-iconic gestures with very high accuracy, and could successfully detect all iconic gestures in our validation dataset. Importantly, SPUDNIG’s output can directly be imported into commonly used annotation tools such as ELAN and ANVIL. We therefore believe that SPUDNIG will be highly relevant for researchers studying multimodal communication due to its annotations significantly accelerating the analysis of large video corpora.

Abstract

Children perceive iconic gestures, along with speech they hear. Previous studies have shown
that children integrate information from both modalities. Yet it is not known whether children
can integrate both types of information simultaneously as soon as they are available as adults
do or processes them separately initially and integrate them later. Using electrophysiological
measures, we examined the online neurocognitive processing of gesture-speech integration in
6- to 7-year-old children. We focused on the N400 event-related potentials component which
is modulated by semantic integration load. Children watched video clips of matching or
mismatching gesture-speech combinations, which varied the semantic integration load. The
ERPs showed that the amplitude of the N400 was larger in the mismatching condition than in
the matching condition. This finding provides the first neural evidence that by the ages of 6
or 7, children integrate multimodal semantic information in an online fashion comparable to
that of adults.

Abstract

In face-to-face conversation, recipients might use the bodily movements of the speaker (e.g. gestures) to facilitate language processing. It has been suggested that one way through which this facilitation may happen is prediction. However, for this to be possible, gestures would need to precede speech, and it is unclear whether this is true during natural conversation.
In a corpus of Dutch conversations, we annotated hand gestures that represent semantic information and occurred during questions, and the word(s) which corresponded most closely to the gesturally depicted meaning. Thus, we tested whether representational gestures temporally precede their lexical affiliates. Further, to see whether preceding gestures may indeed facilitate language processing, we asked whether the gesture-speech asynchrony predicts the response time to the question the gesture is part of.
Gestures and their strokes (most meaningful movement component) indeed preceded the corresponding lexical information, thus demonstrating their predictive potential. However, while questions with gestures got faster responses than questions without, there was no evidence that questions with larger gesture-speech asynchronies get faster responses. These results suggest that gestures indeed have the potential to facilitate predictive language processing, but further analyses on larger datasets are needed to test for links between asynchrony and processing advantages.