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

本文提出使用视觉深度神经网络模型进行情感语音识别的新方法，通过使用预先训练的计算机视觉深度模型的迁移学习能力，将声学特征转化为图像进行识别，并在 Berlin EMO-DB 数据集上进行实验，取得了新的最佳效果。