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This work presents a multitask approach to the simultaneous estimation ofage, country of origin, and emotion given vocal burst audio for the 2022 ICMLExpressive Vocalizations Challenge ExVo-MultiTask track. The method of choiceutilized a combination of spectro-temporal modulation and self-supervisedfeatures, followed by an encoder-decoder network organized in a multitaskparadigm. We evaluate the complementarity between the tasks posed by examiningindependent task-specific and joint models, and explore the relative strengthsof different feature sets. We also introduce a simple score fusion mechanism toleverage the complementarity of different feature sets for this task.  We find that robust data preprocessing in conjunction with score fusion overspectro-temporal receptive field and HuBERT models achieved our bestExVo-MultiTask test score of 0.412.

Self-supervision and Learnable STRFs for Age, Emotion, and Country Prediction