# Self context-aware emotion perception on human-robot interaction

arXiv: https://arxiv.org/abs/2401.10946 · HTML: https://mlsanity.com/paper/2401.10946/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [Multi-modal Attention for Speech Emotion Recognition](https://mlsanity.com/paper/2009.04107/) (2020)
- [Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention](https://mlsanity.com/paper/2106.04133/) (2021)
- [Attention Driven Fusion for Multi-Modal Emotion Recognition](https://mlsanity.com/paper/2009.10991/) (2020)
- [Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition](https://mlsanity.com/paper/2004.01973/) (2020)

## Built on by (most cited first, 1 of 1)

- [Emotion-Disentangled Embedding Alignment for Noise-Robust and Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2510.09072/) (2025)

## Abstract (arXiv)

Emotion recognition plays a crucial role in various domains of human-robot interaction. In long-term interactions with humans, robots need to respond continuously and accurately, however, the mainstream emotion recognition methods mostly focus on short-term emotion recognition, disregarding the context in which emotions are perceived. Humans consider that contextual information and different contexts can lead to completely different emotional expressions. In this paper, we introduce self context-aware model (SCAM) that employs a two-dimensional emotion coordinate system for anchoring and re-labeling distinct emotions. Simultaneously, it incorporates its distinctive information retention structure and contextual loss. This approach has yielded significant improvements across audio, video, and multimodal. In the auditory modality, there has been a notable enhancement in accuracy, rising from 63.10% to 72.46%. Similarly, the visual modality has demonstrated improved accuracy, increasing from 77.03% to 80.82%. In the multimodal, accuracy has experienced an elevation from 77.48% to 78.93%. In the future, we will validate the reliability and usability of SCAM on robots through psychology experiments.