Deep learning approaches for speech emotion recognition: State of the art and research challenges
Speech emotion recognition (SER) systems identify emotions from the human voice in the
areas of smart healthcare, driving a vehicle, call centers, automatic translation systems, and …
areas of smart healthcare, driving a vehicle, call centers, automatic translation systems, and …
Continuous human activity classification from FMCW radar with Bi-LSTM networks
Recognition of human movements with radar for ambient activity monitoring is a developed
area of research that yet presents outstanding challenges to address. In real environments …
area of research that yet presents outstanding challenges to address. In real environments …
Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system
This paper focuses on the development of shallow and deep neural networks in the form of
multi-layer perceptron, long-short term memory, and gated recurrent unit to model the short …
multi-layer perceptron, long-short term memory, and gated recurrent unit to model the short …
Spatial-temporal feature fusion neural network for EEG-based emotion recognition
The temporal and spatial information of electroencephalogram (EEG) are essential for the
emotion recognition model to learn the discriminative features. Hence, we propose a novel …
emotion recognition model to learn the discriminative features. Hence, we propose a novel …
From regional to global brain: A novel hierarchical spatial-temporal neural network model for EEG emotion recognition
In this paper, we propose a novel Electroencephalograph (EEG) emotion recognition
method inspired by neuroscience with respect to the brain response to different emotions …
method inspired by neuroscience with respect to the brain response to different emotions …
[HTML][HTML] Character-level neural network for biomedical named entity recognition
M Gridach - Journal of biomedical informatics, 2017 - Elsevier
Biomedical named entity recognition (BNER), which extracts important named entities such
as genes and proteins, is a challenging task in automated systems that mine knowledge in …
as genes and proteins, is a challenging task in automated systems that mine knowledge in …
Computer-assisted pronunciation training: From pronunciation scoring towards spoken language learning
This paper reviews the research approaches used in computer-assisted pronunciation
training (CAPT), addresses the existing challenges, and discusses emerging trends and …
training (CAPT), addresses the existing challenges, and discusses emerging trends and …
Sequential human gait classification with distributed radar sensor fusion
This paper presents different information fusion approaches to classify human gait patterns
and falls in a radar sensors network. The human gaits classified in this work are both …
and falls in a radar sensors network. The human gaits classified in this work are both …
Automatic pronunciation assessment using self-supervised speech representation learning
E Kim, JJ Jeon, H Seo, H Kim - arxiv preprint arxiv:2204.03863, 2022 - arxiv.org
Self-supervised learning (SSL) approaches such as wav2vec 2.0 and HuBERT models have
shown promising results in various downstream tasks in the speech community. In particular …
shown promising results in various downstream tasks in the speech community. In particular …
Uncertainty estimation in deep learning with application to spoken language assessment
A Malinin - 2019 - repository.cam.ac.uk
Since convolutional neural networks (CNNs) achieved top performance on the ImageNet
task in 2012, deep learning has become the preferred approach to addressing computer …
task in 2012, deep learning has become the preferred approach to addressing computer …