  Speech Affect Recognition is a problem of extracting emotional affects from
audio data. Low resource languages corpora are rear and affect recognition is a
difficult task in cross-corpus settings. We present an approach in which the
model is trained on high resource language and fine-tune to recognize affects
in low resource language. We train the model in same corpus setting on SAVEE,
EMOVO, Urdu, and IEMOCAP by achieving baseline accuracy of 60.45, 68.05, 80.34,
and 56.58 percent respectively. For capturing the diversity of affects in
languages cross-corpus evaluations are discussed in detail. We find that
accuracy improves by adding the domain target data into the training data.
Finally, we show that performance is improved for low resource language speech
affect recognition by achieving the UAR OF 69.32 and 68.2 for Urdu and Italian
speech affects.
