[HTML][HTML] Deep learning techniques for speech emotion recognition, from databases to models
The advancements in neural networks and the on-demand need for accurate and near real-
time Speech Emotion Recognition (SER) in human–computer interactions make it …
time Speech Emotion Recognition (SER) in human–computer interactions make it …
Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers
Speech is the most natural way of expressing ourselves as humans. It is only natural then to
extend this communication medium to computer applications. We define speech emotion …
extend this communication medium to computer applications. We define speech emotion …
Pareto multi-task learning
Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously.
However, it is often impossible to find one single solution to optimize all the tasks, since …
However, it is often impossible to find one single solution to optimize all the tasks, since …
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 …
Multimodal speech emotion recognition using audio and text
Speech emotion recognition is a challenging task, and extensive reliance has been placed
on models that use audio features in building well-performing classifiers. In this paper, we …
on models that use audio features in building well-performing classifiers. In this paper, we …
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 …
An ensemble 1D-CNN-LSTM-GRU model with data augmentation for speech emotion recognition
Precise recognition of emotion from speech signals aids in enhancing human–computer
interaction (HCI). The performance of a speech emotion recognition (SER) system depends …
interaction (HCI). The performance of a speech emotion recognition (SER) system depends …
Multi-task semi-supervised adversarial autoencoding for speech emotion recognition
Inspite the emerging importance of Speech Emotion Recognition (SER), the state-of-the-art
accuracy is quite low and needs improvement to make commercial applications of SER …
accuracy is quite low and needs improvement to make commercial applications of SER …
[PDF][PDF] Analysis of Deep Learning Architectures for Cross-Corpus Speech Emotion Recognition.
Abstract Speech Emotion Recognition (SER) is an important and challenging task for human-
computer interaction. In the literature deep learning architectures have been shown to yield …
computer interaction. In the literature deep learning architectures have been shown to yield …
Self supervised adversarial domain adaptation for cross-corpus and cross-language speech emotion recognition
Despite the recent advancement in speech emotion recognition (SER) within a single corpus
setting, the performance of these SER systems degrades significantly for cross-corpus and …
setting, the performance of these SER systems degrades significantly for cross-corpus and …