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In this study, we investigate how different types of masks affect automatic emotion classification in different channels of audio, visual, and multimodal. We train emotion classification models for each modality with the original data without mask and the re-generated data with mask respectively, and investigate how muffled speech and occluded facial expressions change the prediction of emotions. Moreover, we conduct the contribution analysis to study how muffled speech and occluded face interplay with each other and further investigate the individual contribution of audio, visual, and audio-visual modalities to the prediction of emotion with and without mask. Finally, we investigate the cross-corpus emotion recognition across clear speech and re-generated speech with different types of masks, and discuss the robustness of speech emotion recognition. 

In this paper, we investigate various acoustic features and lexical features for the INTERSPEECH 2020 Computational Paralinguistic Challenge. For the acoustic analysis, we show that the proposed FV-MFCC feature is very promising, which has very strong prediction power on its own, and can also provide complementary information when fused with other acoustic features. For the lexical representation, we find that the corpus-dependent TF.IDF feature is by far the best representation. We also explore several model fusion techniques to combine different modalities together, and propose novel SVM models to aggregate the chunk-level predictions to the narrative-level predictions based on the chunk-level decision functionals. Finally we discuss the potential for improving prediction by combining the lexical and acoustic modalities together, and we find that fusion of lexical and acoustic modalities do not lead to consistent improvements over elderly Arousal, but substantially improve over the Valence. Our methods significantly outperform the official baselines on the test set in the participated Mask and Elderly Sub-challenges. We obtain an UAR of 75.1%, 54.3%, and 59.0% on the Mask, Elderly Arousal and Valence prediction tasks respectively.