Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
In recent years, the rapid advances in machine learning (ML) and information fusion has
made it possible to endow machines/computers with the ability of emotion understanding …
made it possible to endow machines/computers with the ability of emotion understanding …
[HTML][HTML] A review on speech emotion recognition using deep learning and attention mechanism
E Lieskovská, M Jakubec, R Jarina, M Chmulík - Electronics, 2021 - mdpi.com
Emotions are an integral part of human interactions and are significant factors in determining
user satisfaction or customer opinion. speech emotion recognition (SER) modules also play …
user satisfaction or customer opinion. speech emotion recognition (SER) modules also play …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
U2Fusion: A unified unsupervised image fusion network
This study proposes a novel unified and unsupervised end-to-end image fusion network,
termed as U2Fusion, which is capable of solving different fusion problems, including multi …
termed as U2Fusion, which is capable of solving different fusion problems, including multi …
MLT-DNet: Speech emotion recognition using 1D dilated CNN based on multi-learning trick approach
S Kwon - Expert Systems with Applications, 2021 - Elsevier
Speech is the most dominant source of communication among humans, and it is an efficient
way for human–computer interaction (HCI) to exchange information. Nowadays, speech …
way for human–computer interaction (HCI) to exchange information. Nowadays, speech …
Survey of deep representation learning for speech emotion recognition
Traditionally, speech emotion recognition (SER) research has relied on manually
handcrafted acoustic features using feature engineering. However, the design of …
handcrafted acoustic features using feature engineering. However, the design of …
A survey of speech emotion recognition in natural environment
While speech emotion recognition (SER) has been an active research field since the last
three decades, the techniques that deal with the natural environment have only emerged in …
three decades, the techniques that deal with the natural environment have only emerged in …
Personalized multitask learning for predicting tomorrow's mood, stress, and health
While accurately predicting mood and wellbeing could have a number of important clinical
benefits, traditional machine learning (ML) methods frequently yield low performance in this …
benefits, traditional machine learning (ML) methods frequently yield low performance in this …
Attentive convolutional neural network based speech emotion recognition: A study on the impact of input features, signal length, and acted speech
Speech emotion recognition is an important and challenging task in the realm of human-
computer interaction. Prior work proposed a variety of models and feature sets for training a …
computer interaction. Prior work proposed a variety of models and feature sets for training a …
[HTML][HTML] Impact of feature selection algorithm on speech emotion recognition using deep convolutional neural network
Speech emotion recognition (SER) plays a significant role in human–machine interaction.
Emotion recognition from speech and its precise classification is a challenging task because …
Emotion recognition from speech and its precise classification is a challenging task because …