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New-age conversational agent systems perform both speech emotion recognition(SER) and automatic speech recognition (ASR) using two separate and oftenindependent approaches for real-world application in noisy environments. Inthis paper, we investigate a joint ASR-SER multitask learning approach in alow-resource setting and show that improvements are observed not only in SER,but also in ASR. We also investigate the robustness of such jointly trainedmodels to the presence of background noise, babble, and music. Experimentalresults on the IEMOCAP dataset show that joint learning can improve ASR worderror rate (WER) and SER classification accuracy by 10.7% and 2.3% respectivelyin clean scenarios. In noisy scenarios, results on data augmented with MUSANshow that the joint approach outperforms the independent ASR and SER approachesacross many noisy conditions. Overall, the joint ASR-SER approach yielded morenoise-resistant models than the independent ASR and SER approaches.

On the Efficacy and Noise-Robustness of Jointly Learned Speech Emotion and Automatic Speech Recognition