Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge
More than a decade has passed since research on automatic recognition of emotion from
speech has become a new field of research in line with its 'big brothers' speech and speaker …
speech has become a new field of research in line with its 'big brothers' speech and speaker …
Categorical and dimensional affect analysis in continuous input: Current trends and future directions
H Gunes, B Schuller - Image and Vision Computing, 2013 - Elsevier
In the context of affective human behavior analysis, we use the term continuous input to refer
to naturalistic settings where explicit or implicit input from the subject is continuously …
to naturalistic settings where explicit or implicit input from the subject is continuously …
3-D convolutional recurrent neural networks with attention model for speech emotion recognition
M Chen, X He, J Yang, H Zhang - IEEE Signal Processing …, 2018 - ieeexplore.ieee.org
Speech emotion recognition (SER) is a difficult task due to the complexity of emotions. The
SER performances are heavily dependent on the effectiveness of emotional features …
SER performances are heavily dependent on the effectiveness of emotional features …
Evaluating deep learning architectures for speech emotion recognition
Abstract Speech Emotion Recognition (SER) can be regarded as a static or dynamic
classification problem, which makes SER an excellent test bed for investigating and …
classification problem, which makes SER an excellent test bed for investigating and …
The Geneva minimalistic acoustic parameter set (GeMAPS) for voice research and affective computing
Work on voice sciences over recent decades has led to a proliferation of acoustic
parameters that are used quite selectively and are not always extracted in a similar fashion …
parameters that are used quite selectively and are not always extracted in a similar fashion …
Speech emotion recognition using deep convolutional neural network and discriminant temporal pyramid matching
Speech emotion recognition is challenging because of the affective gap between the
subjective emotions and low-level features. Integrating multilevel feature learning and model …
subjective emotions and low-level features. Integrating multilevel feature learning and model …
Learning affective features with a hybrid deep model for audio–visual emotion recognition
Emotion recognition is challenging due to the emotional gap between emotions and audio-
visual features. Motivated by the powerful feature learning ability of deep neural networks …
visual features. Motivated by the powerful feature learning ability of deep neural networks …
Attentive convolutional neural network based speech emotion recognition: A study on the impact of input features, signal length, and acted speech
Speech emotion recognition is an important and challenging task in the realm of human-
computer interaction. Prior work proposed a variety of models and feature sets for training a …
computer interaction. Prior work proposed a variety of models and feature sets for training a …
Human‐Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks
AA Alnuaim, M Zakariah, A Alhadlaq… - Computational …, 2022 - Wiley Online Library
Emotions play an essential role in human relationships, and many real‐time applications
rely on interpreting the speaker's emotion from their words. Speech emotion recognition …
rely on interpreting the speaker's emotion from their words. Speech emotion recognition …
[HTML][HTML] Speech emotion recognition using fusion of three multi-task learning-based classifiers: HSF-DNN, MS-CNN and LLD-RNN
Speech emotion recognition plays an increasingly important role in emotional computing
and is still a challenging task due to its complexity. In this study, we developed a framework …
and is still a challenging task due to its complexity. In this study, we developed a framework …