Speech emotion recognition has been a prevalent research topic in recent years. Existing speech emotion recognition approaches mainly involve processing and analyzing speech signals, in order to discern the speaker’s emotions in speech. 2D Gabor filters have been used to extract the spectro-temporal features of the emotional information from spectrogram. We used Gabor filters to find and extract the major feature patterns for different emotions in spectrogram in our previous study which had concentrated on the parts of a sentence that demonstrated intensive expressions of emotions. In this paper, however, we further categorize emotional expressions in a sentence into primary and secondary ones according to its intensiveness and reveal the feature patterns of the secondary emotional expressions in spectrogram. We conducted feature extraction using Gabor filters on the feature patterns of both primary and secondary emotional expressions. Our experimental results outperformed those from the state-of-the-art and primary-patterns-focused algorithms. This demonstrates that secondary emotional feature patterns can be extracted and used to further improve the accuracy of speech emotion recognition.@en

Speech emotion recognition (SER) poses one of the major challenges in human-machine interaction. We propose a new algorithm, the Voiced Segment Selection (VSS) algorithm, which can produce an accurate segmentation of speech signals. The VSS algorithm deals with the voiced signal segment as the texture image processing feature which is different from the traditional method. It uses the Log-Gabor filters to extract the voiced and unvoiced features from spectrogram to make the classification. The finding shows that the VSS method is a more accurate algorithm for voiced segment detection. Therefore, it has potential to improve performance of emotion recognition from speech.@en