Emotion Recognition and Generation: A Comprehensive Review of Face, Speech, and Text Modalities

TL;DR

This comprehensive review covers recent advances in emotion recognition and generation across face, speech, and text modalities, highlighting methodologies, challenges, and future directions to improve human-computer interaction.

Contribution

It provides an integrated overview of recent multimodal emotion recognition and generation research, categorizing approaches and discussing theoretical foundations and evaluation metrics.

Findings

Summarizes state-of-the-art techniques across modalities

Identifies key challenges and limitations in current methods

Proposes future research directions for robustness and ethics

Abstract

Emotion recognition and generation have emerged as crucial topics in Artificial Intelligence research, playing a significant role in enhancing human-computer interaction within healthcare, customer service, and other fields. Although several reviews have been conducted on emotion recognition and generation as separate entities, many of these works are either fragmented or limited to specific methodologies, lacking a comprehensive overview of recent developments and trends across different modalities. In this survey, we provide a holistic review aimed at researchers beginning their exploration in emotion recognition and generation. We introduce the fundamental principles underlying emotion recognition and generation across facial, vocal, and textual modalities. This work categorises recent state-of-the-art research into distinct technical approaches and explains the theoretical foundations and motivations behind these methodologies, offering a clearer understanding of their application. Moreover, we discuss evaluation metrics, comparative analyses, and current limitations, shedding light on the challenges faced by researchers in the field. Finally, we propose future research directions to address these challenges and encourage further exploration into developing robust, effective, and ethically responsible emotion recognition and generation systems.