# Transfer Learning based Speech Affect Recognition in Urdu

arXiv: https://arxiv.org/abs/2103.03580 · HTML: https://mlsanity.com/paper/2103.03580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages](https://mlsanity.com/paper/1812.10411/) (2018)
- [Transfer Learning for Improving Speech Emotion Classification Accuracy](https://mlsanity.com/paper/1801.06353/) (2018)
- [A Siamese Neural Network with Modified Distance Loss For Transfer Learning in Speech Emotion Recognition](https://mlsanity.com/paper/2006.03001/) (2020)

## Abstract (arXiv)

It has been established that Speech Affect Recognition for low resource languages is a difficult task. Here we present a Transfer learning based Speech Affect Recognition approach in which: we pre-train a model for high resource language affect recognition task and fine tune the parameters for low resource language using Deep Residual Network. Here we use standard four data sets to demonstrate that transfer learning can solve the problem of data scarcity for Affect Recognition task. We demonstrate that our approach is efficient by achieving 74.7 percent UAR on RAVDESS as source and Urdu data set as a target. Through an ablation study, we have identified that pre-trained model adds most of the features information, improvement in results and solves less data issues. Using this knowledge, we have also experimented on SAVEE and EMO-DB data set by setting Urdu as target language where only 400 utterances of data is available. This approach achieves high Unweighted Average Recall (UAR) when compared with existing algorithms.