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Speech emotion recognition approaches: A systematic review
The speech emotion recognition (SER) field has been active since it became a crucial
feature in advanced Human–Computer Interaction (HCI), and wide real-life applications use …
feature in advanced Human–Computer Interaction (HCI), and wide real-life applications use …
Seismic shot gather denoising by using a supervised-deep-learning method with weak dependence on real noise data: A solution to the lack of real noise data
In recent years, supervised-deep-learning methods have shown some advantages over
conventional methods in seismic data denoising, such as higher signal-to-noise ratio after …
conventional methods in seismic data denoising, such as higher signal-to-noise ratio after …
[HTML][HTML] An ongoing review of speech emotion recognition
User emotional status recognition is becoming a key feature in advanced Human Computer
Interfaces (HCI). A key source of emotional information is the spoken expression, which may …
Interfaces (HCI). A key source of emotional information is the spoken expression, which may …
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 …
Deep imbalanced learning for multimodal emotion recognition in conversations
The main task of multimodal emotion recognition in conversations (MERC) is to identify the
emotions in modalities, eg, text, audio, image, and video, which is a significant development …
emotions in modalities, eg, text, audio, image, and video, which is a significant development …
Der-gcn: Dialog and event relation-aware graph convolutional neural network for multimodal dialog emotion recognition
With the continuous development of deep learning (DL), the task of multimodal dialog
emotion recognition (MDER) has recently received extensive research attention, which is …
emotion recognition (MDER) has recently received extensive research attention, which is …
Hierarchical dynamic graph convolutional network with interpretability for EEG-based emotion recognition
Graph convolutional networks (GCNs) have shown great prowess in learning topological
relationships among electroencephalogram (EEG) channels for EEG-based emotion …
relationships among electroencephalogram (EEG) channels for EEG-based emotion …
An octonion-based nonlinear echo state network for speech emotion recognition in Metaverse
While the Metaverse is becoming a popular trend and drawing much attention from
academia, society, and businesses, processing cores used in its infrastructures need to be …
academia, society, and businesses, processing cores used in its infrastructures need to be …
Der-gcn: Dialogue and event relation-aware graph convolutional neural network for multimodal dialogue emotion recognition
With the continuous development of deep learning (DL), the task of multimodal dialogue
emotion recognition (MDER) has recently received extensive research attention, which is …
emotion recognition (MDER) has recently received extensive research attention, which is …
Generative adversarial network based synthetic data training model for lightweight convolutional neural networks
Inadequate training data is a significant challenge for deep learning techniques, particularly
in applications where data is difficult to get, and publicly available datasets are uncommon …
in applications where data is difficult to get, and publicly available datasets are uncommon …