13 accepted papers
Depression represents a global mental health challenge requiring efficient and reliable automated detection methods. Current Transformer- or Graph Neural Networks (GNNs)-based multimodal depression detection methods face significant challenges in modeling individual differences and cross-modal tempo
Automatic robotic facial expression generation is crucial for human–robot interaction (HRI), as handcrafted methods based on fixed joint configurations often yield rigid and unnatural behaviors. Although recent automated techniques reduce the need for manual tuning, they tend to fall short by not ad
The prevalence of depression is escalating, especially among youth, which has become a critical mental health concern. Current assessment methods, relying heavily on questionnaires, clinical observations, and AI-driven analyses, are limited by their focus on single-event data, failing to encapsulate…
This study investigates the feasibility of understanding conversation dynamics in multi-party interactions without relying on auditory cues, focusing on continuous eye gaze as a key non-verbal communication modality. Without converting gaze into binary features, the study aims to explore the richnes
Facial mimicry is crucial for human-like robots in human-robot interaction. The challenge is that the high diversity of facial expressions proposes difficulties in programming a robotic face to mimic human facial expressions using traditional methods. In this paper, we present a data-driven method t…
The prediction of dialogue acts (DA) labels on utterance-level in conversations can be treated as a sequence labeling problem, which requires context- and speaker-aware semantic comprehension, especially for Japanese. In this study, we pro-posed a hierarchical attention with the graph neural network…
Typically, trajectories considered anomalous are the ones deviating from usual (e.g., traffic-dictated) driving patterns. However, this closed-set context fails to recognize the unknown anomalous trajectories, resulting in an insufficient self-motivated learning paradigm. In this study, we investiga…
Identifying intentions is a critical task for comprehending the actions of others, anticipating their future behavior, and making informed decisions. However, it is challenging to recognize intentions due to the uncertainty of future human activities and the complex influence factors. In this work,…
As a type of body language, gestures can largely affect the impressions of human-like robots perceived by users. Recent data-driven approaches to the generation of co-speech gestures have successfully promoted the naturalness of produced gestures. These approaches also possess greater generalizabili…
In this paper, we propose an adversarial auto-encoder-based classifier, which can regularize the distribution of latent representation to smooth the boundaries among categories. Moreover, we adopt multi-instance learning by dividing speech into a bag of segments to capture the most salient moments f…
Social connectedness is vital for developing group cohesion and strengthening belongingness. However, with the accelerating pace of modern life, people have fewer opportunities to participate in group-building activities. Furthermore, owing to the teleworking and quarantine requirements necessitated
We propose a probabilistic model that generates nod motions based on utterance categories estimated from the speech input. The model comprises two main blocks. In the first block, dialogue act-related categories are estimated from the input speech. Considering the correlations between dialogue acts…
In order to solve the problems of current hearing aid devices, we make use of environment sensor network, and propose a hearing support system, where individual target and anti-target sound sources in the environment can be selected, and spatial information of the target sound sources is reconstruct…