Spoken emotion recognition is a multidisciplinary research area that has received increasing attention over the last few years. In this paper, restricted Boltzmann machines and deep belief networks are used to classify emotions in speech. The motivation lies in the recent success reported using these alternative techniques in speech processing and speech recognition. This classifier is compared...

Optimal Automatic Speech Recognition takes place when the evaluation is done under circumstances identical to those in which the recognition system was trained. In the speech applications demanded in the actual real world this will almost never happen. There are several variability sources which produce mismatches between the training and test conditions. Depending on his physical or emotional ...

In this paper, we consider both speaker dependent and listener dependent aspects in the assessment of emotions in speech. We model the speaker dependencies in emotional speech production by two parameters which describe the individual’s emotional expression behavior. Similarly, we model the listener’s emotion perception behavior by a simple parametric model. These models form a basis for improv...

Both in the performative arts and in emotion research, professional actors are assumed to be capable of delivering emotions comparable to spontaneous emotional expressions. This study examines the effects of acting training on vocal emotion depiction and recognition. We predicted that professional actors express emotions in a more realistic fashion than non-professional actors. However, profess...

Recently, the importance of reacting to the emotional state of a user has been generally accepted in the field of human-computer interaction and especially speech has received increased focus as a modality from which to automatically deduct information on emotion. So far, mainly academic and not very application-oriented offline studies based on previously recorded and annotated databases with ...

In this paper we present text dependent speaker recognition with an enhancement of detecting the emotion of the speaker prior using the hybrid FFBN and GMM methods. The emotional state of the speaker influences recognition system. Mel-frequency Cepstral Coefficient (MFCC) feature set is used for experimentation. To recognize the emotional state of a speaker Gaussian Mixture Model (GMM) is used ...

This paper presents a speech emotion recognition system using a recurrent neural network (RNN) model trained by an efficient learning algorithm. The proposed system takes into account the long-range contextual effect and the uncertainty of emotional label expressions. To extract high-level representation of emotional states with regard to its temporal dynamics, a powerful learning method with a...

Speech Emotion Recognition (SER) is a hot research topic in the field of Human Computer Interaction (HCI). In this paper, we recognize three emotional states: happy, sad and neutral. The explored features include: energy, pitch, linear predictive spectrum coding (LPCC), mel-frequency spectrum coefficients (MFCC), and mel-energy spectrum dynamic coefficients (MEDC). A German Corpus (Berlin Datab...