# Transferable Positive/Negative Speech Emotion Recognition via Class-wise Adversarial Domain Adaptation

arXiv: https://arxiv.org/abs/1810.12782 · HTML: https://mlsanity.com/paper/1810.12782/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation](https://mlsanity.com/paper/2109.02915/) (2021)
- [MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition](https://mlsanity.com/paper/2308.04025/) (2023)
- [Semi-supervised cross-lingual speech emotion recognition](https://mlsanity.com/paper/2207.06767/) (2022)
- [Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition](https://mlsanity.com/paper/2412.01171/) (2024)

## Abstract (arXiv)

Speech emotion recognition plays an important role in building more intelligent and human-like agents. Due to the difficulty of collecting speech emotional data, an increasingly popular solution is leveraging a related and rich source corpus to help address the target corpus. However, domain shift between the corpora poses a serious challenge, making domain shift adaptation difficult to function even on the recognition of positive/negative emotions. In this work, we propose class-wise adversarial domain adaptation to address this challenge by reducing the shift for all classes between different corpora. Experiments on the well-known corpora EMODB and Aibo demonstrate that our method is effective even when only a very limited number of target labeled examples are provided.