Emotion feature extraction is the key to speech emotional recognition. And ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating emotion mode mixing present in the original empirical mode decomposition(EMD). To evaluate the performance of this new method, this paper investigates the effect of a parameters pertinent to EEMD: speech emotional envelope. First...

Speech emotion recognition is an interesting and challenging speech technology, which can be applied to broad areas. In this paper, we propose to fuse the global statistical and segmental spectral features at the decision level for speech emotion recognition. Each emotional utterance is individually scored by two recognition systems, the global statistics-based and segmental spectrum-based syst...

Recently, increasing attention has been directed to the study of the emotional content of speech signals, and hence, many systems have been proposed to identify the emotional content of a spoken utterance. This paper is a survey of speech emotion classification addressing three important aspects of the design of a speech emotion recognition system. The first one is the choice of suitable featur...

The problem of inferring human emotional state automatically from speech has become one the central problems in Man Machine Interaction (MMI). Though Support Vector Machines (SVMs) were used several worksfor emotion recognition speech, potential using probabilistic SVMs for this task is not explored. emphasis current work on how to use efficient emotions speech. Emotional corpuses two Dravidian...

There are multiple reasons to expect that recognising the verbal content of emotional speech will be a difficult problem, and recognition rates reported in the literature are in fact low. Including information about prosody improves recognition rate for emotions simulated by actors, but its relevance to the freer patterns of spontaneous speech is unproven. This paper shows that recognition rate...

Emotion in speech is an issue that has been attracting the interest of the speech community for many years, both in the context of speech synthesis as well as in automatic speech recognition (ASR). In spite of the remarkable recent progress in Large Vocabulary Recognition (LVR), it is still far behind the ultimate goal of recognising free conversational speech uttered by any speaker in any envi...

Emotional speech recognition is a multidisciplinary research area that has received increasing attention over the last few years. The present paper considers the application of restricted Boltzmann machines (RBM) and deep belief networks (DBN) to the difficult task of automatic Spanish emotional speech recognition. The principal motivation lies in the success reported in a growing body of work ...

Speech signal in addition to the linguistic information contains additional information about the speaker: age, gender, social status, accent (foreign accent, dialects, etc.), emotional state, health etc. Some of these informational channels induce changes of the speech acoustic characteristics. This article presents evaluation of the ASR acoustic models (first trained on neutral, read speech) ...

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...