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In this paper, we argue that singing voice (song) is more emotional than speech. We evaluate different features sets, feature types, and classifiers on both song and speech emotion recognition. Three feature sets: GeMAPS, pyAudioAnalysis, and LibROSA; two feature types, low-level descriptors and high-level statistical functions; and four classifiers: multilayer perceptron, LSTM, GRU, and convolution neural networks; are examined on both songand speech data with the same parameter values. The results show no remarkable difference between song and speech data on using the same method. Comparisons of two results reveal that song is more emotional than speech. In addition, high-level statistical functions of acoustic features gained higher performance than low-level descriptors in this classification task. This result strengthens the previous finding on the regression task which reported the advantage use of high-level features.

On The Differences Between Song and Speech Emotion Recognition: Effect of Feature Sets, Feature Types, and Classifiers