# Transfer Learning for Improving Speech Emotion Classification Accuracy

arXiv: https://arxiv.org/abs/1801.06353 · HTML: https://mlsanity.com/paper/1801.06353/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2018.

## Built on by (most cited first, 10 of 10)

- [Speech Emotion Recognition using Self-Supervised Features](https://mlsanity.com/paper/2202.03896/) (2022)
- [Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation](https://mlsanity.com/paper/2108.02510/) (2021)
- [Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models](https://mlsanity.com/paper/2202.08974/) (2022)
- [A Preliminary Study on Augmenting Speech Emotion Recognition using a Diffusion Model](https://mlsanity.com/paper/2305.11413/) (2023)
- [Knowledge Transfer For On-Device Speech Emotion Recognition with Neural Structured Learning](https://mlsanity.com/paper/2210.14977/) (2022)
- [An Overview of Indian Spoken Language Recognition from Machine Learning Perspective](https://mlsanity.com/paper/2212.03812/) (2022)
- [Transfer learning from High-Resource to Low-Resource Language Improves Speech Affect Recognition Classification Accuracy](https://mlsanity.com/paper/2103.11764/) (2021)
- [Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition](https://mlsanity.com/paper/2104.01978/) (2021)
- [Do sound event representations generalize to other audio tasks? A case study in audio transfer learning](https://mlsanity.com/paper/2106.11335/) (2021)
- [Transfer Learning based Speech Affect Recognition in Urdu](https://mlsanity.com/paper/2103.03580/) (2021)

## Abstract (arXiv)

The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from same corpus collected under the same conditions. The performance of such systems has been shown to drop significantly in cross-corpus and cross-language scenarios. To address the problem, this paper exploits a transfer learning technique to improve the performance of speech emotion recognition systems that is novel in cross-language and cross-corpus scenarios. Evaluations on five different corpora in three different languages show that Deep Belief Networks (DBNs) offer better accuracy than previous approaches on cross-corpus emotion recognition, relative to a Sparse Autoencoder and SVM baseline system. Results also suggest that using a large number of languages for training and using a small fraction of the target data in training can significantly boost accuracy compared with baseline also for the corpus with limited training examples.