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

• URL: http://arxiv.org/abs/2206.12568v1
• Date: Sat, 25 Jun 2022 06:09:10 GMT
• Title: Self-supervision and Learnable STRFs for Age, Emotion, and Country Prediction
• Authors: Roshan Sharma, Tyler Vuong, Mark Lindsey, Hira Dhamyal, Rita Singh and Bhiksha Raj
• Abstract summary: This work presents a multitask approach to the simultaneous estimation of age, country of origin, and emotion given vocal burst audio. 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 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.
• Score: 26.860736835176617
• License: http://creativecommons.org/licenses/by/4.0/
• 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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