A review on speech emotion recognition using deep learning and attention mechanism
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 …
End-to-end multimodal emotion recognition using deep neural networks
Automatic affect recognition is a challenging task due to the various modalities emotions can
be expressed with. Applications can be found in many domains including multimedia …
be expressed with. Applications can be found in many domains including multimedia …
Human–computer interaction with a real-time speech emotion recognition with ensembling techniques 1D convolution neural network and attention
W Alsabhan - Sensors, 2023 - mdpi.com
Emotions have a crucial function in the mental existence of humans. They are vital for
identifying a person's behaviour and mental condition. Speech Emotion Recognition (SER) …
identifying a person's behaviour and mental condition. Speech Emotion Recognition (SER) …
Attention based fully convolutional network for speech emotion recognition
Speech emotion recognition is a challenging task for three main reasons: 1) human emotion
is abstract, which means it is hard to distinguish; 2) in general, human emotion can only be …
is abstract, which means it is hard to distinguish; 2) in general, human emotion can only be …
Big data for industry 4.0: A conceptual framework
Exponential growth in data volume originating from Internet of Things sources and
information services drives the industry to develop new models and distributed tools to …
information services drives the industry to develop new models and distributed tools to …
A multi-task learning framework for emotion recognition using 2D continuous space
Dimensional models have been proposed in psychology studies to represent complex
human emotional expressions. Activation and valence are two common dimensions in such …
human emotional expressions. Activation and valence are two common dimensions in such …
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 …
Multi-head attention fusion networks for multi-modal speech emotion recognition
J Zhang, L **ng, Z Tan, H Wang, K Wang - Computers & Industrial …, 2022 - Elsevier
Multi-modal speech emotion recognition is a study to predict emotion categories by
combining speech data with other types of data, such as video, speech text transcription …
combining speech data with other types of data, such as video, speech text transcription …
Multi-channel weight-sharing autoencoder based on cascade multi-head attention for multimodal emotion recognition
Multimodal Emotion Recognition is challenging because of the heterogeneity gap among
different modalities. Due to the powerful ability of feature abstraction, Deep Neural Networks …
different modalities. Due to the powerful ability of feature abstraction, Deep Neural Networks …
Convolutional attention networks for multimodal emotion recognition from speech and text data
WY Choi, KY Song, CW Lee - Proceedings of grand challenge …, 2018 - aclanthology.org
Emotion recognition has become a popular topic of interest, especially in the field of human
computer interaction. Previous works involve unimodal analysis of emotion, while recent …
computer interaction. Previous works involve unimodal analysis of emotion, while recent …