Abstract
Speech emotion recognition (SER) is a difficult task because emotions are subjective and recognizing the affective state of the speaker is challenging. To tackle this issue, Broad Learning System is presented to balance the training of networks that are substantially faster than those used previously. Furthermore, we performed experiments on the standard IEMOCAP dataset and achieved the state-of-the-art performance in terms of weighted accuracy and unweighted accuracy. Taken together, the experimental results demonstrated that applying Broad Learning System to SER is reasonable and useful.
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