# Transfer learning from High-Resource to Low-Resource Language Improves Speech Affect Recognition Classification Accuracy

arXiv: https://arxiv.org/abs/2103.11764 · HTML: https://mlsanity.com/paper/2103.11764/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages](https://mlsanity.com/paper/1812.10411/) (2018)
- [Low-Resource Speech-to-Text Translation](https://mlsanity.com/paper/1803.09164/) (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)

## Built on by (most cited first, 1 of 1)

- [Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization](https://mlsanity.com/paper/2304.14535/) (2023)

## Abstract (arXiv)

Speech Affect Recognition is a problem of extracting emotional affects from audio data. Low resource languages corpora are rear and affect recognition is a difficult task in cross-corpus settings. We present an approach in which the model is trained on high resource language and fine-tune to recognize affects in low resource language. We train the model in same corpus setting on SAVEE, EMOVO, Urdu, and IEMOCAP by achieving baseline accuracy of 60.45, 68.05, 80.34, and 56.58 percent respectively. For capturing the diversity of affects in languages cross-corpus evaluations are discussed in detail. We find that accuracy improves by adding the domain target data into the training data. Finally, we show that performance is improved for low resource language speech affect recognition by achieving the UAR OF 69.32 and 68.2 for Urdu and Italian speech affects.