Prediction of Who Will Be the Next Speaker and When Using Gaze Behavior in Multiparty Meetings

Trimodal prediction of speaking and listening willingness to help improve turn-changing modeling

Participants in a conversation must carefully monitor the turn-management (speaking and listening) willingness of other conversational partners and adjust their turn-changing behaviors accordingly to have smooth conversation. Many studies have focused on developing actual turn-changing (i.e., next speaker or end-of-turn) models that can predict whether turn-keeping or turn-changing will occur. Participants' verbal and non-verbal behaviors have been used as input features for predictive models. To the best of our knowledge, these studies only model the relationship between participant behavior and turn-changing. Thus, there is no model that takes into account participants' willingness to acquire a turn (turn-management willingness). In this paper, we address the challenge of building such models to predict the willingness of both speakers and listeners. Firstly, we find that dissonance exists between willingness and actual turn-changing. Secondly, we propose predictive models that are based on trimodal inputs, including acoustic, linguistic, and visual cues distilled from conversations. Additionally, we study the impact of modeling willingness to help improve the task of turn-changing prediction. To do so, we introduce a dyadic conversation corpus with annotated scores of speaker/listener turn-management willingness. Our results show that using all three modalities (i.e., acoustic, linguistic, and visual cues) of the speaker and listener is critically important for predicting turn-management willingness. Furthermore, explicitly adding willingness as a prediction task improves the performance of turn-changing prediction. Moreover, turn-management willingness prediction becomes more accurate when this joint prediction of turn-management willingness and turn-changing is performed by using multi-task learning techniques.

Automatic Visual Attention Detection for Mobile Eye Tracking Using Pre-Trained Computer Vision Models and Human Gaze

Processing visual stimuli in a scene is essential for the human brain to make situation-aware decisions. These stimuli, which are prevalent subjects of diagnostic eye tracking studies, are commonly encoded as rectangular areas of interest (AOIs) per frame. Because it is a tedious manual annotation task, the automatic detection and annotation of visual attention to AOIs can accelerate and objectify eye tracking research, in particular for mobile eye tracking with egocentric video feeds. In this work, we implement two methods to automatically detect visual attention to AOIs using pre-trained deep learning models for image classification and object detection. Furthermore, we develop an evaluation framework based on the VISUS dataset and well-known performance metrics from the field of activity recognition. We systematically evaluate our methods within this framework, discuss potentials and limitations, and propose ways to improve the performance of future automatic visual attention detection methods.

A Differential Approach for Gaze Estimation

Most non-invasive gaze estimation methods regress gaze directions directly from a single face or eye image. However, due to important variabilities in eye shapes and inner eye structures amongst individuals, universal models obtain limited accuracies and their output usually exhibit high variance as well as subject dependent biases. Thus, increasing accuracy is usually done through calibration, allowing gaze predictions for a subject to be mapped to her actual gaze. In this article, we introduce a novel approach, which works by directly training a differential convolutional neural network to predict gaze differences between two eye input images of the same subject. Then, given a set of subject specific calibration images, we can use the inferred differences to predict the gaze direction of a novel eye sample. The assumption is that by comparing eye images of the same user, annoyance factors (alignment, eyelid closing, illumination perturbations) which usually plague single image prediction methods can be much reduced, allowing better prediction altogether. Furthermore, the differential network itself can be adapted via finetuning to make predictions consistent with the available user reference pairs. Experiments on 3 public datasets validate our approach which constantly outperforms state-of-the-art methods even when using only one calibration sample or those relying on subject specific gaze adaptation.

Using Respiration to Predict Who Will Speak Next and When in Multiparty Meetings

Techniques that use nonverbal behaviors to predict turn-changing situations—such as, in multiparty meetings, who the next speaker will be and when the next utterance will occur—have been receiving a lot of attention in recent research. To build a model for predicting these behaviors we conducted a research study to determine whether respiration could be effectively used as a basis for the prediction. Results of analyses of utterance and respiration data collected from participants in multiparty meetings reveal that the speaker takes a breath more quickly and deeply after the end of an utterance in turn-keeping than in turn-changing. They also indicate that the listener who will be the next speaker takes a bigger breath more quickly and deeply in turn-changing than the other listeners. On the basis of these results, we constructed and evaluated models for predicting the next speaker and the time of the next utterance in multiparty meetings. The results of the evaluation suggest that the characteristics of the speaker's inhalation right after an utterance unit—the points in time at which the inhalation starts and ends after the end of the utterance unit and the amplitude, slope, and duration of the inhalation phase—are effective for predicting the next speaker in multiparty meetings. They further suggest that the characteristics of listeners' inhalation—the points in time at which the inhalation starts and ends after the end of the utterance unit and the minimum and maximum inspiration, amplitude, and slope of the inhalation phase—are effective for predicting the next speaker. The start time and end time of the next speaker's inhalation are also useful for predicting the time of the next utterance in turn-changing.