A systematic review of trimodal affective computing approaches: Text, audio, and visual integration in emotion recognition and sentiment analysis
At the heart of affective computing lies the crucial task of decoding human emotions, a field
that expertly intertwines emotion identification with the nuances of sentiment analysis. This …
that expertly intertwines emotion identification with the nuances of sentiment analysis. This …
A survey of deep learning-based multimodal emotion recognition: Speech, text, and face
Multimodal emotion recognition (MER) refers to the identification and understanding of
human emotional states by combining different signals, including—but not limited to—text …
human emotional states by combining different signals, including—but not limited to—text …
Data augmentation for audio-visual emotion recognition with an efficient multimodal conditional GAN
Audio-visual emotion recognition is the research of identifying human emotional states by
combining the audio modality and the visual modality simultaneously, which plays an …
combining the audio modality and the visual modality simultaneously, which plays an …
Deep learning-based speech emotion recognition using multi-level fusion of concurrent features
The detection and classification of emotional states in speech involves the analysis of audio
signals and text transcriptions. There are complex relationships between the extracted …
signals and text transcriptions. There are complex relationships between the extracted …
Harris hawks sparse auto-encoder networks for automatic speech recognition system
Automatic speech recognition (ASR) is an effective technique that can convert human
speech into text format or computer actions. ASR systems are widely used in smart …
speech into text format or computer actions. ASR systems are widely used in smart …
A systematic review on multimodal emotion recognition: building blocks, current state, applications, and challenges
Emotion recognition involves accurately interpreting human emotions from various sources
and modalities, including questionnaires, verbal, and physiological signals. With its broad …
and modalities, including questionnaires, verbal, and physiological signals. With its broad …
Emotion ontology studies: A framework for expressing feelings digitally and its application to sentiment analysis
Emotion ontologies have been developed to capture affect, a concept that encompasses
discrete emotions and feelings, especially for research on sentiment analysis, which …
discrete emotions and feelings, especially for research on sentiment analysis, which …
Machine learning algorithms for detection and classifications of emotions in contact center applications
Over the past few years, virtual assistant solutions used in Contact Center systems are
gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions …
gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions …
Multimodal emotion recognition: A comprehensive review, trends, and challenges
MPA Ramaswamy… - … Reviews: Data Mining and …, 2024 - Wiley Online Library
Automatic emotion recognition is a burgeoning field of research and has its roots in
psychology and cognitive science. This article comprehensively reviews multimodal emotion …
psychology and cognitive science. This article comprehensively reviews multimodal emotion …
Deep learning approaches for bimodal speech emotion recognition: Advancements, challenges, and a multi-learning model
Though acoustic speech emotion recognition has been studied for a while, bimodal speech
emotion recognition using both acoustic and text has gained momentum since speech …
emotion recognition using both acoustic and text has gained momentum since speech …