相关论文: Emotions Beyond Words: Non-Speech Audio Emotion Re…

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,…

We examine the utility of implicit user behavioral signals captured using low-cost, off-the-shelf devices for anonymous gender and emotion recognition. A user study designed to examine male and female sensitivity to facial emotions confirms…

Emotions are subjective constructs. Recent end-to-end speech emotion recognition systems are typically agnostic to the subjective nature of emotions, despite their state-of-the-art performance. In this work, we introduce an end-to-end…

The emotion recognition in conversation (ERC) task aims to predict the emotion label of an utterance in a conversation. Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies,…

Emotion recognition and sentiment analysis are pivotal tasks in speech and language processing, particularly in real-world scenarios involving multi-party, conversational data. This paper presents a multimodal approach to tackle these…

We need open platforms driven by specialists, in which queries can be created and collected for long periods and the diagnosis made, based on a rigorous clinical follow-up. In this work, we developed a multi-language robot interface helping…

This paper focuses on finding suitable features to robustly recognize emotions and evaluate customer satisfaction from speech in real acoustic scenarios. The classification of emotions is based on standard and well-known corpora and the…

For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets have been generated with the use of…

One of the most important study areas in affective computing is emotion identification using EEG data. In this study, the Gated Recurrent Unit (GRU) algorithm, which is a type of Recurrent Neural Networks (RNNs), is tested to see if it can…

Emotional expressions are inherently multimodal -- integrating facial behavior, speech, and gaze -- but their automatic recognition is often limited to a single modality, e.g. speech during a phone call. While previous work proposed…

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…

Emotion is well-recognized as a distinguished symbol of human beings, and it plays a crucial role in our daily lives. Existing vision-based or sensor-based solutions are either obstructive to use or rely on specialized hardware, hindering…

Speech emotion recognition (SER) systems are constrained by existing datasets that typically cover only 6-10 basic emotions, lack scale and diversity, and face ethical challenges when collecting sensitive emotional states. We introduce…

In this work, user's emotion using its facial expressions will be detected. These expressions can be derived from the live feed via system's camera or any pre-exisiting image available in the memory. Emotions possessed by humans can be…

People naturally understand emotions, thus permitting a machine to do the same could open new paths for human-computer interaction. Facial expressions can be very useful for emotion recognition techniques, as these are the biggest…

Intelligent monitoring systems and affective computing applications have emerged in recent years to enhance healthcare. Examples of these applications include assessment of affective states such as Major Depressive Disorder (MDD). MDD…

Artificial Intelligence (AI) has been used for processing data to make decisions, interact with humans, and understand their feelings and emotions. With the advent of the internet, people share and express their thoughts on day-to-day…

Speaker recognition systems are widely used in various applications to identify a person by their voice; however, the high degree of variability in speech signals makes this a challenging task. Dealing with emotional variations is very…

Compared to other modalities, electroencephalogram (EEG) based emotion recognition can intuitively respond to emotional patterns in the human brain and, therefore, has become one of the most focused tasks in affective computing. The nature…

Automated emotion recognition in speech is a long-standing problem. While early work on emotion recognition relied on hand-crafted features and simple classifiers, the field has now embraced end-to-end feature learning and classification…