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 …
Speech emotion recognition using deep learning techniques: A review
Emotion recognition from speech signals is an important but challenging component of
Human-Computer Interaction (HCI). In the literature of speech emotion recognition (SER) …
Human-Computer Interaction (HCI). In the literature of speech emotion recognition (SER) …
Automated emotion recognition: Current trends and future perspectives
Background Human emotions greatly affect the actions of a person. The automated emotion
recognition has applications in multiple domains such as health care, e-learning …
recognition has applications in multiple domains such as health care, e-learning …
Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning
Due to the complex and intricate nature associated with their production, the acoustic-
prosodic properties of a speech signal are modulated with a range of health related effects …
prosodic properties of a speech signal are modulated with a range of health related effects …
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 …
Deep representation learning in speech processing: Challenges, recent advances, and future trends
Research on speech processing has traditionally considered the task of designing hand-
engineered acoustic features (feature engineering) as a separate distinct problem from the …
engineered acoustic features (feature engineering) as a separate distinct problem from the …
Semi-supervised speech emotion recognition with ladder networks
S Parthasarathy, C Busso - IEEE/ACM transactions on audio …, 2020 - ieeexplore.ieee.org
Speech emotion recognition (SER) systems find applications in various fields such as
healthcare, education, and security and defense. A major drawback of these systems is their …
healthcare, education, and security and defense. A major drawback of these systems is their …
Multi-resolution modulation-filtered cochleagram feature for LSTM-based dimensional emotion recognition from speech
Continuous dimensional emotion recognition from speech helps robots or virtual agents
capture the temporal dynamics of a speaker's emotional state in natural human–robot …
capture the temporal dynamics of a speaker's emotional state in natural human–robot …
A weakly supervised learning framework for detecting social anxiety and depression
Although social anxiety and depression are common, they are often underdiagnosed and
undertreated, in part due to difficulties identifying and accessing individuals in need of …
undertreated, in part due to difficulties identifying and accessing individuals in need of …
Few-shot learning for fine-grained emotion recognition using physiological signals
Fine-grained emotion recognition can model the temporal dynamics of emotions, which is
more precise than predicting one emotion retrospectively for an activity (eg, video clip …
more precise than predicting one emotion retrospectively for an activity (eg, video clip …