One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy. In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods, such as the Fisher and PCA. In this research, a hybrid evolutionary algorit...

This paper describes and evaluates four different HSMM (hidden semi-Markov model) training methods for HMM-based synthesis of emotional speech. The first method, called emotion-dependent modelling, uses individual models trained for each emotion separately. In the second method, emotion adaptation modelling, at first a model is trained using neutral speech, and thereafter adaptation is performe...

Accurate emotion recognition from speech is important for applications like smart health care, smart entertainment, and other smart services. High accuracy emotion recognition from Chinese speech is challenging due to the complexities of the Chinese language. In this paper, we explore how to improve the accuracy of speech emotion recognition, including speech signal feature extraction and emoti...

A wavelet packet based adaptive filter-bank construction combined with Deep Belief Network(DBN) feature learning method is proposed for speech signal processing in this paper. On this basis, a set of acoustic features are extracted for speech emotion recognition, namely Coiflet Wavelet Packet Cepstral Coefficients (CWPCC). CWPCC extends the conventional MelFrequency Cepstral Coefficients (MFCC)...

This paper introduces a first approach to emotion recognition using RAMSES, the UPC’s speech recognition system. The approach is based on standard speech recognition technology using hidden semi-continuous Markov models. Both the selection of low level features and the design of the recognition system are addressed. Results are given on speaker dependent emotion recognition using the Spanish co...

Cross-corpus speech emotion recognition(SER) is a hot topic in classification. SER includes these four issues:feature selection, differences constraint, label regression and preservation of discriminative features. Seldom literature can solve issues jointly previous studies.In this work,we propose the transfer emotion-discriminative features subspace learning(TEDFSL) method.Acoustic are extract...

This article address a novel emotion recognition system based on the Truncated Gaussian mixture model .The proposed system has been experimented over an gender independent emotion recognition database In the recent past, many models have been listed in the literature based on the emotion recognition, but these papers are more focused towards the speech, ignoring the emotion of the speaker at th...

Analysis of speech for recognition of stress is important for identification of emotional state of person. This can be done using ‘Linear Techniques’, which has different parameters like pitch, vocal tract spectrum, formant frequencies, Duration, MFCC etc. which are used for extraction of features from speech. TEO-CB-Auto-Env is the method which is non-linear method of features extraction. Anal...

Abstract--The main objective of this research is to develop a speech emotion recognition system using residual phase and MFCC features with autoassociative neural network (AANN). The speech emotion recognition system classifies the speech emotion into predefined categories such as anger, fear, happy, neutral or sad. The proposed technique for speech emotion recognition (SER) has two phases : Fe...

Automated emotion recognition has a number of applications in Interactive Voice Response systems, call centers, etc. While employing existing feature sets and methods for automated emotion recognition has already achieved reasonable results, there is still a lot to do for improvement. Meanwhile, an optimal feature set, which should be used to represent speech signals for performing speech-based...