  We propose a novel multi-task pre-training method for Speech Emotion
Recognition (SER). We pre-train SER model simultaneously on Automatic Speech
Recognition (ASR) and sentiment classification tasks to make the acoustic ASR
model more ``emotion aware''. We generate targets for the sentiment
classification using text-to-sentiment model trained on publicly available
data. Finally, we fine-tune the acoustic ASR on emotion annotated speech data.
We evaluated the proposed approach on the MSP-Podcast dataset, where we
achieved the best reported concordance correlation coefficient (CCC) of 0.41
for valence prediction.
