GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition

Spectro-temporal Modulations for Robust Speech Emotion Recognition Spectro-temporal Modulations for Robust Speech Emotion Recognition

Speech Emotion Recognition Using Scalogram Based Deep Structure

Speech Emotion Recognition (SER) is an important part of speech-based Human-Computer Interface (HCI) applications. Previous SER methods rely on the extraction of features and training an appropriate classifier. However, most of those features can be affected by emotionally irrelevant factors such as gender, speaking styles and environment. Here, an SER method has been proposed based on a concat...

Emotion recognition of conversational affective speech using temporal course modeling

In a natural conversation, a complete emotional expression is typically composed of a complex temporal course representing temporal phases of onset, apex, and offset. In this study, subemotional states are defined to model the temporal course of an emotional expression in natural conversation. Hidden Markov Models (HMMs) are adopted to characterize the subemotional states; each represents one t...

Modeling Perceivers Neural-Responses Using Lobe-Dependent Convolutional Neural Network to Improve Speech Emotion Recognition

Developing automatic emotion recognition by modeling expressive behaviors is becoming crucial in enabling the next generation design of human-machine interface. Also, with the availability of functional magnetic resonance imaging (fMRI), researchers have also conducted studies into quantitative understanding of vocal emotion perception mechanism. In this work, our aim is two folds: 1) investiga...

Emotion recognition using imperfect speech recognition

This paper investigates the use of speech-to-text methods for assigning an emotion class to a given speech utterance. Previous work shows that an emotion extracted from text can convey complementary evidence to the information extracted by classifiers based on spectral, or other non-linguistic features. As speech-to-text usually presents significantly more computational effort, in this study we...