Many paralinguistic tasks are closely related and thus representations
learned in one domain can be leveraged for another. In this paper, we
investigate how knowledge can be transferred between three paralinguistic
tasks: speaker, emotion, and gender recognition. Further, we extend this
problem to cross-dataset tasks, asking how knowledge captured in one emotion
dataset can be transferred to another. We focus on progressive neural networks
and compare these networks to the conventional deep learning method of
pre-training and fine-tuning. Progressive neural networks provide a way to
transfer knowledge and avoid the forgetting effect present when pre-training
neural networks on different tasks. Our experiments demonstrate that: (1)
emotion recognition can benefit from using representations originally learned
for different paralinguistic tasks and (2) transfer learning can effectively
leverage additional datasets to improve the performance of emotion recognition
systems.

本研究探讨如何在说话人识别、情感识别和性别识别三个语音非语言任务之间转移知识，并将其扩展到交叉数据集任务，比较使用渐进神经网络与预训练和微调的传统深度学习方法。结果表明，表情识别可以受益于使用最初学习不同语音非语言任务的表示，并且转移学习可以有效地利用其他数据集来提高情感识别系统的性能。