Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: A Systematic Review of Task Formulations and Machine Learning Methods
Empathy indicates an individual's ability to understand others. Over the past few years, empathy has drawn attention from various disciplines, including but not limited to Affective Computing, Cognitive Science, and Psychology. Detecting empathy has potential applications in society, healthcare and education. Despite being a broad and overlapping topic, the avenue of empathy detection leveraging Machine Learning remains underexplored from a systematic literature review perspective. We collected 849 papers from 10 well-known academic databases, systematically screened them and analysed the final 82 papers. Our analyses reveal several prominent task formulations - including empathy on localised utterances or overall expressions, unidirectional or parallel empathy, and emotional contagion - in monadic, dyadic and group interactions. Empathy detection methods are summarised based on four input modalities - text, audiovisual, audio and physiological signals - thereby presenting modality-specific network architecture design protocols. We discuss challenges, research gaps and potential applications in the Affective Computing-based empathy domain, which can facilitate new avenues of exploration. We further enlist the public availability of datasets and codes. This paper, therefore, provides a structured overview of recent advancements and remaining challenges towards developing a robust empathy detection system that could meaningfully contribute to enhancing human well-being.
Code (1)
Tasks
Privacy PreservingSystematic Literature ReviewSimilar Papers 제목 키워드 기반
Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset
This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The da…
Audiovisual SlowFast Networks for Video Recognition
We present Audiovisual SlowFast Networks, an architecture for integrated audiovisual perception. AVSlowFast has Slow and Fast visual pathways that are deeply integrated with a Faster Audio pathway to model vision and sou…
Action ClassificationVideo RecognitionImproving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion
It is challenging to recognize facial action unit (AU) from spontaneous facial displays, especially when they are accompanied by speech. The major reason is that the information is extracted from a single source, i.e., t…
Facial Action Unit DetectionDetecting Adversarial Attacks On Audiovisual Speech Recognition
Adversarial attacks pose a threat to deep learning models. However, research on adversarial detection methods, especially in the multi-modal domain, is very limited. In this work, we propose an efficient and straightforw…
Audio-Visual Speech Recognitionspeech-recognitionSpeech RecognitionVisual Speech RecognitionPushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learning
We introduce Perception Encoder Audiovisual, PE-AV, a new family of encoders for audio and video understanding trained with scaled contrastive learning. Built on PE, PE-AV makes several key contributions to extend repres…
Sound Event DetectionContrastive Learning