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In this work, we tackle a problem of speech emotion classification. One ofthe issues in the area of affective computation is that the amount of annotateddata is very limited. On the other hand, the number of ways that the sameemotion can be expressed verbally is enormous due to variability betweenspeakers. This is one of the factors that limits performance andgeneralization. We propose a simple method that extracts audio samples frommovies using textual sentiment analysis. As a result, it is possible toautomatically construct a larger dataset of audio samples with positive,negative emotional and neutral speech. We show that pretraining recurrentneural network on such a dataset yields better results on the challengingEmotiW corpus. This experiment shows a potential benefit of combining textualsentiment analysis with vocal information.

Automatically augmenting an emotion dataset improves classification using audio