Advancing User-Voice Interaction: Exploring Emotion-Aware Voice Assistants Through a Role-Swapping Approach

TL;DR

This paper investigates how users emotionally regulate during voice assistant interactions using a role-swapping approach, analyzing speech and language patterns to inform the development of empathetic, adaptive, and culturally sensitive voice assistants.

Contribution

It introduces a novel role-swapping method to study human emotional responses in VA interactions, providing insights for designing emotionally intelligent voice assistants.

Findings

Participants prefer neutral or positive responses to negative cues.

Acoustic features like RMS, ZCR, and jitter are sensitive to emotional states.

Sentiment polarity and lexical diversity distinguish emotional responses.

Abstract

As voice assistants (VAs) become increasingly integrated into daily life, the need for emotion-aware systems that can recognize and respond appropriately to user emotions has grown. While significant progress has been made in speech emotion recognition (SER) and sentiment analysis, effectively addressing user emotions-particularly negative ones-remains a challenge. This study explores human emotional response strategies in VA interactions using a role-swapping approach, where participants regulate AI emotions rather than receiving pre-programmed responses. Through speech feature analysis and natural language processing (NLP), we examined acoustic and linguistic patterns across various emotional scenarios. Results show that participants favor neutral or positive emotional responses when engaging with negative emotional cues, highlighting a natural tendency toward emotional regulation and de-escalation. Key acoustic indicators such as root mean square (RMS), zero-crossing rate (ZCR), and jitter were identified as sensitive to emotional states, while sentiment polarity and lexical diversity (TTR) distinguished between positive and negative responses. These findings provide valuable insights for developing adaptive, context-aware VAs capable of delivering empathetic, culturally sensitive, and user-aligned responses. By understanding how humans naturally regulate emotions in AI interactions, this research contributes to the design of more intuitive and emotionally intelligent voice assistants, enhancing user trust and engagement in human-AI interactions.