Related papers: MUSER: MUltimodal Stress Detection using Emotion R…
In the current age, human lifestyle has become more knowledge oriented leading to generation of sedentary employment. This has given rise to a number of health and mental disorders. Mental wellness is one of the most neglected but crucial…
Multimodal speech emotion recognition (SER) has emerged as pivotal for improving human-machine interaction. Researchers are increasingly leveraging both speech and textual information obtained through automatic speech recognition (ASR) to…
Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of sentiment and emotion, as well as physiological-emotion and emotion-based stress recognition through more comprehensively integrating the audio-visual,…
Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that…
In this paper, we propose MMER, a novel Multimodal Multi-task learning approach for Speech Emotion Recognition. MMER leverages a novel multimodal network based on early-fusion and cross-modal self-attention between text and acoustic…
Symbolic Music Emotion Recognition(SMER) is to predict music emotion from symbolic data, such as MIDI and MusicXML. Previous work mainly focused on learning better representation via (mask) language model pre-training but ignored the…
In this paper, we extensively present our solutions for the MuSe-Stress sub-challenge and the MuSe-Physio sub-challenge of Multimodal Sentiment Challenge (MuSe) 2021. The goal of MuSe-Stress sub-challenge is to predict the level of…
Identifying and understanding underlying sentiment or emotions in text is a key component of multiple natural language processing applications. While simple polarity sentiment analysis is a well-studied subject, fewer advances have been…
Multimodal emotion recognition is an important research topic in artificial intelligence, whose main goal is to integrate multimodal clues to identify human emotional states. Current works generally assume accurate labels for benchmark…
Human-Computer Interaction (HCI) is a multi-modal, interdisciplinary field focused on designing, studying, and improving the interactions between people and computer systems. This involves the design of systems that can recognize,…
Stress can be seen as a physiological response to everyday emotional, mental and physical challenges. A long-term exposure to stressful situations can have negative health consequences, such as increased risk of cardiovascular diseases and…
Affective computing has garnered the attention and interest of researchers in recent years, as there is a need for AI systems to better understand and react to human emotions. However, analyzing human emotions, such as mood or stress, is…
Analyzing individual emotions during group conversation is crucial in developing intelligent agents capable of natural human-machine interaction. While reliable emotion recognition techniques depend on different modalities (text, audio,…
Expressing and identifying emotions through facial and physical expressions is a significant part of social interaction. Emotion recognition is an essential task in computer vision due to its various applications and mainly for allowing a…
Emotion plays a fundamental role in human interaction, and therefore systems capable of identifying emotions in speech are crucial in the context of human-computer interaction. Speech emotion recognition (SER) is a challenging problem,…
In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges. The MuSe 2022 focuses on humor detection, emotional reactions and…
The Multimodal Sentiment Analysis Challenge (MuSe) 2022 is dedicated to multimodal sentiment and emotion recognition. For this year's challenge, we feature three datasets: (i) the Passau Spontaneous Football Coach Humor (Passau-SFCH)…
Traditional psychological evaluations rely heavily on human observation and interpretation, which are prone to subjectivity, bias, fatigue, and inconsistency. To address these limitations, this work presents a multimodal emotion recognition…
Psychological stress detection is an important task for mental healthcare research, but there has been little prior work investigating the effectiveness of psychological stress models on minority individuals, who are especially vulnerable…
Related tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both. The…