Speech emotion recognition is an important and challenging task in the realm
of human-computer interaction. Prior work proposed a variety of models and
feature sets for training a system. In this work, we conduct extensive
experiments using an attentive convolutional neural network with multi-view
learning objective function. We compare system performance using different
lengths of the input signal, different types of acoustic features and different
types of emotion speech (improvised/scripted). Our experimental results on the
Interactive Emotional Motion Capture (IEMOCAP) database reveal that the
recognition performance strongly depends on the type of speech data independent
of the choice of input features. Furthermore, we achieved state-of-the-art
results on the improvised speech data of IEMOCAP.

本文介绍了使用具有多视角学习目标函数的关注卷积神经网络进行语音情感识别的实验研究，对比了不同输入信号长度、不同类型的声学特征和不同类型的情感语音 (即兴 / 编剧) 对系统表现的影响，结果显示，除了输入特征选择外，模型的性能强烈依赖于语音数据类型，进一步实现了 IEMOCAP 上即兴语音数据的最优结果。