arXiv subjects

Denis Gračanin

Publications and source records attributed to Denis Gračanin.

3 recordsLinked to original sources

arXiv2026

EmoSay: Artificial Intelligence-Driven Text-to-Emotional-Speech System for Affective Communication in Extended Reality

While contemporary neural text-to-speech (TTS) systems have achieved high levels of intelligibility, they frequently lack the emotional nuance required for authentic affective communication. This limitation is particularly critical in Extended Reality (XR), where the absence of emotionally expressive audio can diminish user presence and spatial immersion. We present EmoSay, an Artificial Intelligence-driven Text-to-Emotional-Speech (TTES) system designed to bridge the semantic-affective gap in immersive environments. EmoSay modulates a neural synthesis pipeline using discrete emotional prompts, delivering the output through a Unity-based interface featuring high-fidelity spatialized audio. The system was evaluated through a comprehensive user study focusing on perception, engagement, and the subjective sense of empathy. Our results demonstrate that EmoSay significantly enhances the immersive experience, achieving a System Usability Scale (SUS) score of 74.76, indicating strong usability and seamless integration within the XR workflow. Subjective assessments reveal a high degree of perceived naturalness and a strong positive correlation between emotional expressiveness and user engagement. Regression analysis identifies vocal naturalness as the strongest of the tested predictors of user satisfaction, suggesting that EmoSay's affective prosody helps meet the heightened expectations for realism in immersive settings. This work contributes a scalable, affect-aware framework for inclusive XR design and demonstrates the role synthetic emotion can play in fostering human-computer rapport through voice-first interaction.

cs.HC↗

arXiv2026

Exploring Expert Perspectives on Wearable-Triggered LLM Conversational Support for Daily Stress Management

Wearable devices increasingly support stress detection, while LLMs enable conversational mental health support. However, designing systems that meaningfully connect wearable-triggered stress events with generative dialogue remains underexplored, particularly from a design perspective. We present EmBot, a functional mobile application that combines wearable-triggered stress detection with LLM-based conversational support for daily stress management. We used EmBot as a design probe in semi-structured interviews with 15 mental health experts to examine their perspectives and surface early design tensions and considerations that arise from wearable-triggered conversational support, informing the future design of such systems for daily stress management and mental health support.

cs.HC↗

arXiv2024

Integrating Physiological Data with Large Language Models for Empathic Human-AI Interaction

This paper explores enhancing empathy in Large Language Models (LLMs) by integrating them with physiological data. We propose a physiological computing approach that includes developing deep learning models that use physiological data for recognizing psychological states and integrating the predicted states with LLMs for empathic interaction. We showcase the application of this approach in an Empathic LLM (EmLLM) chatbot for stress monitoring and control. We also discuss the results of a pilot study that evaluates this EmLLM chatbot based on its ability to accurately predict user stress, provide human-like responses, and assess the therapeutic alliance with the user.

eess.SP↗