Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …

Research on speech processing has traditionally considered the task of designing hand-
engineered acoustic features (feature engineering) as a separate distinct problem from the …

Traditionally, speech emotion recognition (SER) research has relied on manually
handcrafted acoustic features using feature engineering. However, the design of …

Speech emotion recognition is an important but difficult task in human–computer interaction
systems. One of the main challenges in speech emotion recognition is how to extract …

Speech Emotion Recognition (SER) system, which analyzes human utterances to determine
a speaker's emotion, has a growing impact on how people and machines interact. Recent …

Modeling dependencies between climate extremes is important for climate risk assessment,
for instance when allocating emergency management funds. In statistics, multivariate …

Speech emotion recognition (SER) is an important research area, with direct impacts in
applications of our daily lives, spanning education, health care, security and defense …

Traditional audio steganography by cover modification causes changes to the cover features
during the embedding of a secret, which is easy to detect with emerging neural-network …

We present a Multi-Window Data Augmentation (MWA-SER) approach for speech emotion
recognition. MWA-SER is a unimodal approach that focuses on two key concepts; designing …

Deep learning models have been applied in baby cry recognition to enhance the recognition
accuracy. However, the current research still suffers from data imbalance problem, which …