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In this paper, we propose a new methodology for emotional speech recognitionusing visual deep neural network models. We employ the transfer learningcapabilities of the pre-trained computer vision deep models to have a mandatefor the emotion recognition in speech task. In order to achieve that, wepropose to use a composite set of acoustic features and a procedure to convertthem into images. Besides, we present a training paradigm for these modelstaking into consideration the different characteristics between acoustic-basedimages and regular ones. In our experiments, we use the pre-trained VGG-16model and test the overall methodology on the Berlin EMO-DB dataset forspeaker-independent emotion recognition. We evaluate the proposed model on thefull list of the seven emotions and the results set a new state-of-the-art.

Emotional Speech Recognition with Pre-trained Deep Visual Models