# Unsupervised Cross-Lingual Speech Emotion Recognition Using DomainAdversarial Neural Network

arXiv: https://arxiv.org/abs/2012.11174 · HTML: https://mlsanity.com/paper/2012.11174/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Semi-supervised cross-lingual speech emotion recognition](https://mlsanity.com/paper/2207.06767/) (2022)
- [Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised Models](https://mlsanity.com/paper/2409.16920/) (2024)
- [SPEED: A Scalable RISC-V Vector Processor Enabling Efficient Multi-Precision DNN Inference](https://mlsanity.com/paper/2409.14017/) (2024)

## Abstract (arXiv)

By using deep learning approaches, Speech Emotion Recog-nition (SER) on a single domain has achieved many excellentresults. However, cross-domain SER is still a challenging taskdue to the distribution shift between source and target domains.In this work, we propose a Domain Adversarial Neural Net-work (DANN) based approach to mitigate this distribution shiftproblem for cross-lingual SER. Specifically, we add a languageclassifier and gradient reversal layer after the feature extractor toforce the learned representation both language-independent andemotion-meaningful. Our method is unsupervised, i. e., labelson target language are not required, which makes it easier to ap-ply our method to other languages. Experimental results showthe proposed method provides an average absolute improve-ment of 3.91% over the baseline system for arousal and valenceclassification task. Furthermore, we find that batch normaliza-tion is beneficial to the performance gain of DANN. Thereforewe also explore the effect of different ways of data combinationfor batch normalization.