The rise of nonnegative matrix factorization: algorithms and applications
YT Guo, QQ Li, CS Liang - Information Systems, 2024 - Elsevier
Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …
methods result in misleading results and waste of computing resources due to lack of timely …
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 …
A review on speech emotion recognition: a survey, recent advances, challenges, and the influence of noise
SM George, PM Ilyas - Neurocomputing, 2024 - Elsevier
Affective Computing systems can detect the emotional state and mindset of an individual.
Speech Emotion Recognition (SER) is a unimodal affect computing system based on …
Speech Emotion Recognition (SER) is a unimodal affect computing system based on …
Speech emotion recognition based on convolutional neural network with attention-based bidirectional long short-term memory network and multi-task learning
ZT Liu, MT Han, BH Wu, A Rehman - Applied Acoustics, 2023 - Elsevier
Speech emotion recognition (SER) is a challenging task since the distribution of the features
is different among various people. To improve generalization performance and accuracy of …
is different among various people. To improve generalization performance and accuracy of …
A deep interpretable representation learning method for speech emotion recognition
This paper focuses on the active interpretability for deep learning-based speech emotion
recognition (SER). To achieve this, we propose an explicit feature constrained model, the …
recognition (SER). To achieve this, we propose an explicit feature constrained model, the …
Unsupervised feature selection via self-paced learning and low-redundant regularization
Much more attention has been paid to unsupervised feature selection nowadays due to the
emergence of massive unlabeled data. The distribution of samples and the latent effect of …
emergence of massive unlabeled data. The distribution of samples and the latent effect of …
Multiscale-multichannel feature extraction and classification through one-dimensional convolutional neural network for Speech emotion recognition
M Liu, ANJ Raj, V Rajangam, K Ma, Z Zhuang… - Speech …, 2024 - Elsevier
Speech emotion recognition (SER) is a crucial field of research in artificial intelligence and
human–computer interaction. Extracting effective speech features for emotion recognition is …
human–computer interaction. Extracting effective speech features for emotion recognition is …
Automated speech emotion polarization for a distance education system based on orbital local binary pattern and an appropriate sub-band selection technique
The distance education system was widely adopted during the Covid-19 pandemic by many
institutions of learning. To measure the effectiveness of this system, it is essential to evaluate …
institutions of learning. To measure the effectiveness of this system, it is essential to evaluate …
A robust graph based multi-label feature selection considering feature-label dependency
Y Liu, H Chen, T Li, W Li - Applied Intelligence, 2023 - Springer
Feature selection for multilabel data is a challenging and meaningful work. The information
contained in multilabel data is more abundant, which may help further mine knowledge and …
contained in multilabel data is more abundant, which may help further mine knowledge and …
Speech emotion recognition-a deep learning approach
UA Asiya, VK Kiran - 2021 Fifth International Conference on I …, 2021 - ieeexplore.ieee.org
Speech emotion recognition is a very popular topic of research among researchers. This
research work has implemented a deep learning-based categorization model of emotion …
research work has implemented a deep learning-based categorization model of emotion …