Related papers: Emotion Recognition from Multiple Modalities: Fund…
Emotion recognition has a pivotal role in affective computing and in human-computer interaction. The current technological developments lead to increased possibilities of collecting data about the emotional state of a person. In general,…
Humans are sophisticated at reading interlocutors' emotions from multimodal signals, such as speech contents, voice tones and facial expressions. However, machines might struggle to understand various emotions due to the difficulty of…
In this paper, we address the problem of multimodal emotion recognition from multiple physiological signals. We demonstrate that a Transformer-based approach is suitable for this task. In addition, we present how such models may be…
Understanding the facial expressions of our interlocutor is important to enrich the communication and to give it a depth that goes beyond the explicitly expressed. In fact, studying one's facial expression gives insight into their hidden…
Humans express their emotions via facial expressions, voice intonation and word choices. To infer the nature of the underlying emotion, recognition models may use a single modality, such as vision, audio, and text, or a combination of…
Emotion has an important role in daily life, as it helps people better communicate with and understand each other more efficiently. Facial expressions can be classified into 7 categories: angry, disgust, fear, happy, neutral, sad and…
One of the challenges in affect recognition is accurate estimation of the emotion intensity level. This research proposes development of an affect intensity estimation model based on a weighted sum of classification confidence levels,…
To fully understand the complexities of human emotion, the integration of multiple physical features from different modalities can be advantageous. Considering this, we present an analysis of 3D facial data, action units, and physiological…
The purpose of emotion recognition in conversation (ERC) is to identify the emotion category of an utterance based on contextual information. Previous ERC methods relied on simple connections for cross-modal fusion and ignored the…
This paper presents a Multi-modal Emotion Recognition (MER) system designed to enhance emotion recognition accuracy in challenging acoustic conditions. Our approach combines a modified and extended Hierarchical Token-semantic Audio…
This paper presents an innovative approach to address the challenges of translating multi-modal emotion recognition models to a more practical and resource-efficient uni-modal counterpart, specifically focusing on speech-only emotion…
Emotions are best way of communicating information; and sometimes it carry more information than words. Recently, there has been a huge interest in automatic recognition of human emotion because of its wide spread application in security,…
Multimodal music emotion recognition (MMER) is an emerging discipline in music information retrieval that has experienced a surge in interest in recent years. This survey provides a comprehensive overview of the current state-of-the-art in…
Human emotion is expressed in many communication modalities and media formats and so their computational study is equally diversified into natural language processing, audio signal analysis, computer vision, etc. Similarly, the large…
Emotion classification in text is typically performed with neural network models which learn to associate linguistic units with emotions. While this often leads to good predictive performance, it does only help to a limited degree to…
Affective computing plays a key role in human-computer interactions, entertainment, teaching, safe driving, and multimedia integration. Major breakthroughs have been made recently in the areas of affective computing (i.e., emotion…
Multi-modal Multi-label Emotion Recognition (MMER) aims to identify various human emotions from heterogeneous visual, audio and text modalities. Previous methods mainly focus on projecting multiple modalities into a common latent space and…
Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the…
Affective computing has proven to be a viable field of research comprised of a large number of multidisciplinary researchers resulting in work that is widely published. The majority of this work consists of computational models of emotion…
Emotion recognition has become an important field of research in Human Computer Interactions as we improve upon the techniques for modelling the various aspects of behaviour. With the advancement of technology our understanding of emotions…