Speech Emotion Recognition (SER) is a new and challenging research area with a wide range of applications in man-machine interactions. The aim of a SER system is to recognize human emotion by analyzing the acoustics of speech sound. In this study, we propose Spectral Pattern features (SPs) and Harmonic Energy features (HEs) for emotion recognition. These features extracted from the spectrogram ...

The speech emotion recognition is a very interesting yet challenging task of human computer interaction. In the recent years this topic has grabbed so much attention. field many techniques have been utilized to extract emotions from signals , including well- established analysis and classification techniques. traditional way features are extracted then selected which collectively know as select...

This work introduces the few-shot learning paradigm in speech emotion recognition domain. Emotional characterization of segments is carried out through analogies, i.e. by assessing similarities and dissimilarities between novel known recordings. More specifically, we designed a Siamese Neural Network modeling such relationships on combined log-Mel temporal modulation spectrogram space. We prese...

This study investigates the effects of emotion on different phoneme classes using short-term spectral features. In the research on emotion in speech, most studies have focused on prosodic features of speech. In this study, based on the hypothesis that different emotions have varying effects on the properties of the different speech sounds, we investigate the usefulness of phoneme-class level ac...