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.
@article{arxiv.2201.11826,
title = {Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition},
author = {Ayoub Ghriss and Bo Yang and Viktor Rozgic and Elizabeth Shriberg and Chao Wang},
journal= {arXiv preprint arXiv:2201.11826},
year = {2022}
}