[HTML][HTML] A review of deep transfer learning and recent advancements
Deep learning has been the answer to many machine learning problems during the past two
decades. However, it comes with two significant constraints: dependency on extensive …
decades. However, it comes with two significant constraints: dependency on extensive …
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 …
Deep learning for human affect recognition: Insights and new developments
Automatic human affect recognition is a key step towards more natural human-computer
interaction. Recent trends include recognition in the wild using a fusion of audiovisual and …
interaction. Recent trends include recognition in the wild using a fusion of audiovisual and …
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 …
Improving speech emotion recognition with adversarial data augmentation network
L Yi, MW Mak - IEEE transactions on neural networks and …, 2020 - ieeexplore.ieee.org
When training data are scarce, it is challenging to train a deep neural network without
causing the overfitting problem. For overcoming this challenge, this article proposes a new …
causing the overfitting problem. For overcoming this challenge, this article proposes a new …
Transfer learning for improving speech emotion classification accuracy
The majority of existing speech emotion recognition research focuses on automatic emotion
detection using training and testing data from same corpus collected under the same …
detection using training and testing data from same corpus collected under the same …
Improving speech emotion recognition with unsupervised representation learning on unlabeled speech
In this paper we present our findings on how representation learning on large unlabeled
speech corpora can be beneficially utilized for speech emotion recognition (SER). Prior work …
speech corpora can be beneficially utilized for speech emotion recognition (SER). Prior work …
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 …
All-in-One: Emotion, Sentiment and Intensity Prediction Using a Multi-Task Ensemble Framework
We propose a multi-task ensemble framework that jointly learns multiple related problems.
The ensemble model aims to leverage the learned representations of three deep learning …
The ensemble model aims to leverage the learned representations of three deep learning …
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 …