  It has been established that Speech Affect Recognition for low resource
languages is a difficult task. Here we present a Transfer learning based Speech
Affect Recognition approach in which: we pre-train a model for high resource
language affect recognition task and fine tune the parameters for low resource
language using Deep Residual Network. Here we use standard four data sets to
demonstrate that transfer learning can solve the problem of data scarcity for
Affect Recognition task. We demonstrate that our approach is efficient by
achieving 74.7 percent UAR on RAVDESS as source and Urdu data set as a target.
Through an ablation study, we have identified that pre-trained model adds most
of the features information, improvement in results and solves less data
issues. Using this knowledge, we have also experimented on SAVEE and EMO-DB
data set by setting Urdu as target language where only 400 utterances of data
is available. This approach achieves high Unweighted Average Recall (UAR) when
compared with existing algorithms.
