AI spots gender in how speakers move, gaze and gesture in VR — even when performance looks identical

Public speaking is one of the most universal professional skills — and one of the most gendered. Confidence and persuasion on stage have traditionally been attributed to men, a sociocultural association that can trigger stereotype threat and impose an additional psychological constraint on female speakers in high-stakes situations. Yet whether men and women actually behave differently when presenting, or merely perceive themselves differently, has remained surprisingly unclear. Now, a team at Aix-Marseille University has used virtual reality to disentangle the question with unusual precision, and the results challenge several long-standing assumptions about gender and the podium.

The study, published in Frontiers in Artificial Intelligence, placed 55 participants — 29 men and 26 women aged 18 to 61 — in front of a virtual audience of four characters arranged in a semicircle, delivered through a wireless Meta Quest Pro headset. Each participant gave six short oral presentations on familiar topics such as a personal introduction or a description of their hometown. The experimental design systematically varied two audience characteristics: the gendered appearance of the virtual characters, which could be entirely male, entirely female, or mixed, and the audience’s social attitude, expressed through nonverbal cues such as posture, gaze direction and facial expressions that conveyed either agreement, attentiveness and happiness, or disagreement, distraction and unhappiness.

What made the experiment technically distinctive was its multimodal data capture. The Meta XR Interaction SDK recorded head and hand positions, gaze direction and facial muscle activations derived from FACS blendshapes directly through the headset, used without controllers. An external camera in front of the speaker recorded each session for later expert evaluation. From these raw signals, the researchers extracted 33 interpretable multimodal nonverval behavioral features organized into four categories: gaze features such as gaze diversity and entropy, body movement features including horizontal displacement amplitude, facial expression features tracking ten specific blendshape frequencies, and gesture and posture features covering palm orientations, hand configurations and body openness.

The evaluation framework combined subjective and objective measures. Participants completed the Self-Assessment Manikin to gauge emotional valence and the Personal Report of Confidence as a Speaker to measure anxiety after each presentation, alongside a 7-point self-reported performance rating. An experienced public speaking expert then evaluated all 330 recorded presentations through a dedicated Unity-based replay application, rating overall performance and 20 behavioral items across five dimensions: verbal behavior, gestures, posture, body movements and gaze direction. The order of presentations was randomized to prevent direct comparisons between sessions from the same participant.

Statistical analysis using Aligned Rank Transform ANOVA revealed a striking pattern. The virtual audience’s social attitude significantly influenced participants’ emotional experience and self-reported performance: speakers reported lower anxiety and more positive emotions when facing a positive audience than a negative one, and rated their own performance more favorably after supportive presentations. However, no significant effect of audience gendered appearance was detected on any measure — anxiety, emotional state, self-reported performance or expert-rated performance. More surprisingly, speaker gender showed no significant influence on emotional experience or overall public speaking performance either, contrasting with previous literature reporting higher public speaking anxiety among female speakers.

The expert did, however, identify gender differences in specific nonverbal behaviors. Male speakers displayed more open gestures and appeared more comfortable in terms of body protection behaviors, while female speakers were rated as more static during their presentations and needing greater improvement in gesture use. Notably, the expert found no gender differences in verbal behavior. But these observations could reflect either genuine behavioral variations or subjective perceptions on the part of the evaluator — a distinction the researchers addressed with machine learning.

To determine whether nonverval behaviors objectively differed by gender, the team trained a Multilayer Perceptron classifier to predict speaker gender from the automatically extracted features. Each of the 330 presentations was divided into three equal-duration segments, producing 990 observations split with rigorous stratification so that subjects in the training set never appeared in the test set. Hyperparameter optimization used random search over 10 configurations, with Recursive Feature Elimination combined with 5-fold cross-validation to identify the optimal feature subset. The final model achieved a weighted F1-score of 0.81 and an area under the ROC curve of 0.86 on the independent test set, with the small gap between training and test performance suggesting good generalization and limited overfitting.

SHapley Additive exPlanations (SHAP) analysis then revealed which behavioral features drove the predictions. Horizontal displacement amplitude — the maximum distance a speaker moved along the x-axis — emerged as the most important feature for gender classification. Female speakers tended to exhibit smaller horizontal displacement amplitudes, used neutral palm orientations less frequently while displaying downward-oriented palms more often, and directed their gaze less uniformly toward the audience. Male speakers generally displayed larger displacement amplitudes, more frequent neutral palm orientations, and more balanced gaze behavior distributed evenly across audience members. Female speakers also showed greater variability in facial expressions, particularly through more frequent Upper Lid Raiser movements, an action unit commonly associated with surprise, fear or anger.

The convergence between expert evaluation and automatic analysis is significant: both approaches identified distinct nonverval behavioral patterns between male and female speakers, even though neither found differences in overall performance. The automatic analysis went further, detecting gaze and facial expression differences that the expert could not systematically assess — partly because the VR headset partially obscured speakers’ faces, making facial expression evaluation impractical, and partly because the multi-window replay interface limited accurate tracking of gaze dynamics.

These findings carry important implications for automated public speaking training systems. If comparable levels of performance can be achieved through distinct behavioral profiles, a universal evaluation model may be insufficient to accurately assess speakers or provide effective guidance. Accounting for individual behavioral variability may represent a critical consideration in developing personalized feedback systems. The researchers also caution that while personalized feedback may better reflect individual communication patterns, it could inadvertently reinforce existing gender stereotypes — a trade-off future work must investigate. The study’s limitations include reliance on a single expert evaluator, exclusive focus on nonverval features in the automatic analysis, and an experimental setting that may not have elicited sufficiently high stress levels for gender-related differences in emotional experience to become detectable. Future research could extend the computational approach to verbal and vocal features, design virtual audiences whose gendered appearance is complemented by gender-congruent nonverval behaviors, and examine whether these behavioral differences extend to real-life public speaking situations.

Subject of Research: Neuroscience / AI

Article Title: How gender shapes public speaking in virtual reality: a multimodal analysis

Article References: Ristorcelli, M., Casanova, R., & Ochs, M. (2026). How gender shapes public speaking in virtual reality: a multimodal analysis. Frontiers in Artificial Intelligence, 9, Article 1954571. https://doi.org/10.3389/frai.2026.1954571

Image Credits: AI Generated

DOI: 10.3389/frai.2026.1954571

Keywords: virtual reality, public speaking, gender differences, machine learning, nonverbal behavior, multimodal analysis, SHAP explainability, virtual audience, gender, shapes, public, speaking