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

2022-06-25 06:09:10
Roshan Sharma, Tyler Vuong, Mark Lindsey, Hira Dhamyal, Rita Singh, Bhiksha Raj

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.

Abstract (translated)

URL

https://arxiv.org/abs/2206.12568

PDF

https://arxiv.org/pdf/2206.12568.pdf


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