Spontaneous speech emotion recognition via multiple kernel learning

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 data and thus it can not produce a satisfactory decision function. To address this issue, we apply multiple kernel learning (MKL) algorithm to recognize the spontaneous speech emotion. The experimental results are evaluated on the spontaneous speech emotion database such as FAU Aibo database. Compared to SVM, MKL can achieve better performance on spontaneous speech emotion recognition.