The role of task and acoustic similarity in audio transfer learning: Insights from the speech emotion recognition case

With the rise of deep learning, deep knowledge transfer has emerged as one of the most effective techniques for getting state-of-the-art performance using deep neural networks. A lot of recent research has focused on understanding the mechanisms of transfer learning in the image and language domains. We perform a similar investigation for the case of speech emotion recognition (SER), and conclude that transfer learning for SER is influenced both by the choice of pre-training task and by the differences in acoustic conditions between the upstream and downstream data sets, with the former having a bigger impact. The effect of each factor is isolated by first transferring knowledge between different tasks on the same data, and then from the original data to corrupted versions of it but for the same task. We also demonstrate that layers closer to the input see more adaptation than ones closer to the output in both cases, a finding which explains why previous works often found it necessary to fine-tune all layers during transfer learning.