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

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 …

Multimodal sentiment analysis is an increasingly popular research area, which extends the
conventional language-based definition of sentiment analysis to a multimodal setup where …

The speech emotion recognition (or, classification) is one of the most challenging topics in
data science. In this work, we introduce a new architecture, which extracts mel-frequency …

Emotion recognition from speech signals is an important but challenging component of
Human-Computer Interaction (HCI). In the literature of speech emotion recognition (SER) …

New research into human-computer interaction seeks to consider the consumer's emotional
status to provide a seamless human-computer interface. This would make it possible for …

Recurrent neural networks (RNNs) are capable of learning features and long term
dependencies from sequential and time-series data. The RNNs have a stack of non-linear …

Automatic emotion recognition from speech is a challenging task which relies heavily on the
effectiveness of the speech features used for classification. In this work, we study the use of …

During the last decade, Speech Emotion Recognition (SER) has emerged as an integral
component within Human-computer Interaction (HCI) and other high-end speech processing …

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 …