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In practical situations, the emotional speech utterances are often collected from different devices and conditions, which will obviously affect the recognition performance. To address this issue, in this paper, a novel transfer non-negative matrix factorization (TNMF) method is presented for cross-corpus speech emotion recognition. First, the NMF algorithm is adopted to learn a latent common feature...
Speech emotion recognition has become an active topic in pattern recognition. Specifically, support vector machine (SVM) is an effective classifier due to the application of the nonlinear mapping function, which can map the data into high or ever infinite dimensional feature space. However, a single kernel function might not sufficient to describe the different properties of spontaneous speech emotion...
For speech emotion recognition on cross-corpus, we study the problem of speaker feature adaptation. First, we discuss the existing approaches in adaptive emotional classification from speech signals. Second, the speaker feature adaptive approach is further studied in view of additive emotion feature distortion. Finally we verified our approaches using different cross-languages corpus, including German,...
To enhance the recognition rate of speaker independent speech emotion recognition, a feature selection and feature fusion combination method based on multiple kernel learning is presented. Firstly, multiple kernel learning is used to obtain sparse feature subsets. The features selected at least n times are recombined into another subset named n-subset. The optimal n is determined by 10 cross-validation...
In this paper Shuffled Frog Leaping Algorithm based neural network is used in speech emotion recognition. Speech emotion data is collected and emotional features are analyzed. Shuffled Frog Leaping Algorithm is used to train the random initial data, optimize the connection weights and thresholds of the neural network with a fast network convergence speed. The results show that Shuffled Frog Leaping...
To meet the real world challenges for speech emotion recognition, four emotions for practical use were studied for evaluation of work ability. Elicited speech corpus was collected in a psychology experiment to provide trustable emotion data, acoustic features related to arousal and valence dimensions were selected specially for the practical emotions and a re-compositive GMM method was used for the...
A method of using two-class classifiers in emotion recognition was discussed. Emotion classes were divided into pairs for improved feature selection and optimization. Each pair of emotions was classified by a two-class classifier and the final recognition result was reached by a correlation decoder. Compared with n-class classifier, using two-class classifiers for each pair of emotions is more suitable...
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