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arXiv · 2206.12568

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

Roshan Sharma·Tyler Vuong·Mark Lindsey·Hira Dhamyal·Rita Singh·Bhiksha Raj
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Abstract

This work presents a multitask approach to the simultaneous estimation of age, country of origin, and emotion given vocal burst audio for the 2022 ICML Expressive Vocalizations Challenge ExVo-MultiTask track. The method of choice utilized a combination of spectro-temporal modulation and self-supervised features, followed by an encoder-decoder network organized in a multitask paradigm. We evaluate the complementarity between the tasks posed by examining independent task-specific and joint models, and explore the relative strengths of different feature sets. We also introduce a simple score fusion mechanism to leverage the complementarity of different feature sets for this task. We find that robust data preprocessing in conjunction with score fusion over spectro-temporal receptive field and HuBERT models achieved our best ExVo-MultiTask test score of 0.412.

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Roshan Sharma, Tyler Vuong, Mark Lindsey, Hira Dhamyal, Rita Singh, Bhiksha Raj. 2022-06-25. Self-supervision and Learnable STRFs for Age, Emotion, and Country Prediction. https://arxiv.org/abs/2206.12568

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