# Jointly Predicting Emotion, Age, and Country Using Pre-Trained Acoustic Embedding

arXiv: https://arxiv.org/abs/2207.10333 · HTML: https://mlsanity.com/paper/2207.10333/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2022.

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

- [Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Bursts](https://mlsanity.com/paper/2209.07384/) (2022)
- [The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data](https://mlsanity.com/paper/2403.14048/) (2024)

## Abstract (arXiv)

In this paper, we demonstrated the benefit of using pre-trained model to extract acoustic embedding to jointly predict (multitask learning) three tasks: emotion, age, and native country. The pre-trained model was trained with wav2vec 2.0 large robust model on the speech emotion corpus. The emotion and age tasks were regression problems, while country prediction was a classification task. A single harmonic mean from three metrics was used to evaluate the performance of multitask learning. The classifier was a linear network with two independent layers and shared layers, including the output layers. This study explores multitask learning on different acoustic features (including the acoustic embedding extracted from a model trained on an affective speech dataset), seed numbers, batch sizes, and normalizations for predicting paralinguistic information from speech.