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

arXiv: https://arxiv.org/abs/2206.12568 · HTML: https://mlsanity.com/paper/2206.12568/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 25 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Speech Emotion Recognition using Self-Supervised Features](https://mlsanity.com/paper/2202.03896/) (2022)
- [The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts](https://mlsanity.com/paper/2205.01780/) (2022)
- [Joint gender and age estimation based on speech signals using x-vectors and transfer learning](https://mlsanity.com/paper/2012.01551/) (2020)
- [Learnable Spectro-temporal Receptive Fields for Robust Voice Type Discrimination](https://mlsanity.com/paper/2010.09151/) (2020)

## Built on by (most cited first, 2 of 2)

- [An Efficient Multitask Learning Architecture for Affective Vocal Burst Analysis](https://mlsanity.com/paper/2209.13914/) (2022)
- [EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification Methodologies](https://mlsanity.com/paper/2301.00508/) (2023)

## Abstract (arXiv)

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.