In this paper, we proposed a new feature extraction method for emotion recognition based on the knowledge of the emotion production mechanism in physiology. It was reported by physiacoustist that emotional speech is differently encoded from the normal speech in terms of articulation organs and that emotion information in speech is concentrated in different frequencies caused by the different mo...

-Speech emotion recognition is a rapidly growing field of research that aims to automatically identify emotions from speech signals. This paper presents using machine learning techniques. The study begins by providing an overview the various approaches used in recognition, including feature extraction, selection, and classification. These selected features like pitch, MFCC are compared with exi...

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ens...

Speech signal is a rich source of information and convey more than spoken words, and can be divided into two main groups: linguistic and nonlinguistic. The linguistic aspects of speech include the properties of the speech signal and word sequence and deal with what is being said. The nonlinguistic properties of speech have more to do with talker attributes such as age, gender, dialect, and emot...

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...

The paper introduced the present status of speech emotion recognition. In order to improve the single-mode emotion recognition rate, the bimodal fusion method based on speech and facial expression was proposed. The emotional databases of Chinese speech and facial expressions were established with the noise stimulus and movies evoking subjects' emtion. On the foundation, we analyzed the acoustic...