Publications

Abstract

Conversation requires a substantial amount of coordination between dialogue participants, from managing turn taking to negotiating mutual understanding. Part of this coordination effort surfaces as the reuse of linguistic behaviour across speakers, a process often referred to as alignment. While the presence of linguistic alignment is well documented in the literature, several questions remain open, including the extent to which patterns of reuse across speakers have an impact on the emergence of labelling conventions for novel referents. In this study, we put forward a methodology for automatically detecting shared lemmatised constructions---expressions with a common lexical core used by both speakers within a dialogue---and apply it to a referential communication corpus where participants aim to identify novel objects for which no established labels exist. Our analyses uncover the usage patterns of shared constructions in interaction and reveal that features such as their frequency and the amount of different constructions used for a referent are associated with the degree of object labelling convergence the participants exhibit after social interaction. More generally, the present study shows that automatically detected shared constructions offer a useful level of analysis to investigate the dynamics of reference negotiation in dialogue.

Abstract

Gestures are integral components of face-to-face communication. They unfold over time, often following predictable movement phases of preparation, stroke, and re-
traction. Yet, the prevalent approach to automatic gesture detection treats the problem as binary classification, classifying a segment as either containing a gesture or not, thus failing to capture its inherently sequential and contextual nature. To address this, we introduce a novel framework that reframes the task as a multi-phase sequence labeling problem rather than binary classification. Our model processes sequences of skeletal movements over time windows, uses Transformer encoders to learn contextual embeddings, and leverages Conditional Random Fields to perform sequence labeling. We evaluate our proposal on a large dataset of diverse co-speech gestures in task-oriented face-to-face dialogues. The results consistently demonstrate that our method significantly outperforms strong baseline models in detecting gesture strokes. Furthermore, applying Transformer encoders to learn contextual embeddings from movement sequences substantially improves gesture unit detection. These results highlight our framework’s capacity to capture the fine-grained dynamics of co-speech gesture phases, paving the way for more nuanced and accurate gesture detection and analysis.

Abstract

In face-to-face dialogues, the form-meaning relationship of co-speech gestures varies depending on contextual factors such as what the gestures refer to and the individual characteristics of speakers. These factors make co-speech gesture representation learning challenging. How can we learn meaningful gestures representations considering gestures’ variability and relationship with speech? This paper tackles this challenge by employing self-supervised contrastive learning techniques to learn gesture representations from skeletal and speech information. We propose an approach that includes both unimodal and multimodal pre-training to ground gesture representations in co-occurring speech. For training, we utilize a face-to-face dialogue dataset rich with representational iconic gestures. We conduct thorough intrinsic evaluations of the learned representations through comparison with human-annotated pairwise gesture similarity. Moreover, we perform a diagnostic probing analysis to assess the possibility of recovering interpretable gesture features from the learned representations. Our results show a significant positive correlation with human-annotated gesture similarity and reveal that the similarity between the learned representations is consistent with well-motivated patterns related to the dynamics of dialogue interaction. Moreover, our findings demonstrate that several features concerning the form of gestures can be recovered from the latent representations. Overall, this study shows that multimodal contrastive learning is a promising approach for learning gesture representations, which opens the door to using such representations in larger-scale gesture analysis studies.

Abstract

Purpose

To perform a scoping review to investigate the psychosocial impact of having an altered facial expression in five neurological diseases.
Methods

A systematic literature search was performed. Studies were on Bell’s palsy, facioscapulohumeral muscular dystrophy (FSHD), Moebius syndrome, myotonic dystrophy type 1, or Parkinson’s disease patients; had a focus on altered facial expression; and had any form of psychosocial outcome measure. Data extraction focused on psychosocial outcomes.
Results

Bell’s palsy, myotonic dystrophy type 1, and Parkinson’s disease patients more often experienced some degree of psychosocial distress than healthy controls. In FSHD, facial weakness negatively influenced communication and was experienced as a burden. The psychosocial distress applied especially to women (Bell’s palsy and Parkinson’s disease), and patients with more severely altered facial expression (Bell’s palsy), but not for Moebius syndrome patients. Furthermore, Parkinson’s disease patients with more pronounced hypomimia were perceived more negatively by observers. Various strategies were reported to compensate for altered facial expression.
Conclusions

This review showed that patients with altered facial expression in four of five included neurological diseases had reduced psychosocial functioning. Future research recommendations include studies on observers’ judgements of patients during social interactions and on the effectiveness of compensation strategies in enhancing psychosocial functioning.
Implications for rehabilitation

Negative effects of altered facial expression on psychosocial functioning are common and more abundant in women and in more severely affected patients with various neurological disorders.

Health care professionals should be alert to psychosocial distress in patients with altered facial expression.

Learning of compensatory strategies could be a beneficial therapy for patients with psychosocial distress due to an altered facial expression.

Abstract

During face-to-face conversation, transitions between speaker turns are incredibly fast. These fast turn exchanges seem to involve next speakers predicting upcoming semantic information, such that next turn planning can begin before a current turn is complete. Given that face-to-face conversation also involves the use of communicative bodily signals, an important question is how bodily signals such as co-speech hand gestures play into these processes of prediction and fast responding. In this corpus study, we found that hand gestures that depict or refer to semantic information started before the corresponding information in speech, which held both for the onset of the gesture as a whole, as well as the onset of the stroke (the most meaningful part of the gesture). This early timing potentially allows listeners to use the gestural information to predict the corresponding semantic information to be conveyed in speech. Moreover, we provided further evidence that questions with gestures got faster responses than questions without gestures. However, we found no evidence for the idea that how much a gesture precedes its lexical affiliate (i.e., its predictive potential) relates to how fast responses were given. The findings presented here highlight the importance of the temporal relation between speech and gesture and help to illuminate the potential mechanisms underpinning multimodal language processing during face-to-face conversation.

Abstract

Most language use occurs in face-to-face conversation, which involves rapid turn-taking. Seeing communicative bodily signals in addition to hearing speech may facilitate such fast responding. We tested whether this holds for co-speech hand gestures by investigating whether these gestures speed up button press responses to questions. Sixty native speakers of Dutch viewed videos in which an actress asked yes/no-questions, either with or without a corresponding iconic hand gesture. Participants answered the questions as quickly and accurately as possible via button press. Gestures did not impact response accuracy, but crucially, gestures sped up responses, suggesting that response planning may be finished earlier when gestures are seen. How much gestures sped up responses was not related to their timing in the question or their timing with respect to the corresponding information in speech. Overall, these results are in line with the idea that multimodality may facilitate fast responding during face-to-face conversation.

Abstract

The typical pattern of alternating turns in conversation seems trivial at first sight. But a closer look quickly reveals the cognitive challenges involved, with much of it resulting from the fast-paced nature of conversation. One core ingredient to turn coordination is the anticipation of upcoming turn ends so as to be able to ready oneself for providing the next contribution. Across two experiments, we investigated two variables inherent to face-to-face conversation, the presence of visual bodily signals and preceding discourse context, in terms of their contribution to turn end anticipation. In a reaction time paradigm, participants anticipated conversational turn ends better when seeing the speaker and their visual bodily signals than when they did not, especially so for longer turns. Likewise, participants were better able to anticipate turn ends when they had access to the preceding discourse context than when they did not, and especially so for longer turns. Critically, the two variables did not interact, showing that visual bodily signals retain their influence even in the context of preceding discourse. In a pre-registered follow-up experiment, we manipulated the visibility of the speaker's head, eyes and upper body (i.e. torso + arms). Participants were better able to anticipate turn ends when the speaker's upper body was visible, suggesting a role for manual gestures in turn end anticipation. Together, these findings show that seeing the speaker during conversation may critically facilitate turn coordination in interaction.

Abstract

Human language is extremely versatile, combining a limited set of signals in an unlimited number of ways. However, it is unknown whether conversational visual signals feed into the composite utterances with which speakers communicate their intentions. We assessed whether different combinations of visual signals lead to different intent interpretations of the same spoken utterance. Participants viewed a virtual avatar uttering spoken questions while producing single visual signals (i.e., head turn, head tilt, eyebrow raise) or combinations of these signals. After each video, participants classified the communicative intention behind the question. We found that composite utterances combining several visual signals conveyed different meaning compared to utterances accompanied by the single visual signals. However, responses to combinations of signals were more similar to the responses to related, rather than unrelated, individual signals, indicating a consistent influence of the individual visual signals on the whole. This study therefore provides first evidence for compositional, non-additive (i.e., Gestalt-like) perception of multimodal language.

Abstract

The natural ecology of language is conversation, with individuals taking turns speaking to communicate in a back-and-forth fashion. Language in this context involves strings of words that a listener must process while simultaneously planning their own next utterance. It would thus be highly advantageous if language users distributed information within an utterance in a way that may facilitate this processing–planning dynamic. While some studies have investigated how information is distributed at the level of single words or clauses, or in written language, little is known about how information is distributed within spoken utterances produced during naturalistic conversation. It also is not known how information distribution patterns of spoken utterances may differ across languages. We used a set of matched corpora (CallHome) containing 898 telephone conversations conducted in six different languages (Arabic, English, German, Japanese, Mandarin, and Spanish), analyzing more than 58,000 utterances, to assess whether there is evidence of distinct patterns of information distributions at the utterance level, and whether these patterns are similar or differed across the languages. We found that English, Spanish, and Mandarin typically show a back-loaded distribution, with higher information (i.e., surprisal) in the last half of utterances compared with the first half, while Arabic, German, and Japanese showed front-loaded distributions, with higher information in the first half compared with the last half. Additional analyses suggest that these patterns may be related to word order and rate of noun and verb usage. We additionally found that back-loaded languages have longer turn transition times (i.e.,time between speaker turns)

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.