# Cross-lingual and Multilingual Speech Emotion Recognition on English and French

arXiv: https://arxiv.org/abs/1803.00357 · HTML: https://mlsanity.com/paper/1803.00357/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2018.

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

- [Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection](https://mlsanity.com/paper/2409.10985/) (2024)
- [Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model's Empathy](https://mlsanity.com/paper/2510.10328/) (2025)
- [Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2308.02190/) (2023)
- [Semi-supervised cross-lingual speech emotion recognition](https://mlsanity.com/paper/2207.06767/) (2022)
- [Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data](https://mlsanity.com/paper/2306.17199/) (2023)
- [Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised Models](https://mlsanity.com/paper/2409.16920/) (2024)
- [A Novel Markovian Framework for Integrating Absolute and Relative Ordinal Emotion Information](https://mlsanity.com/paper/2108.04605/) (2021)
- [Persian Emotion Detection using ParsBERT and Imbalanced Data Handling Approaches](https://mlsanity.com/paper/2211.08029/) (2022)
- [Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search](https://mlsanity.com/paper/2211.08237/) (2022)
- [Multilingual Emotion Neurons in Large Audio-Language Models](https://mlsanity.com/paper/2608.08772/) (2026)

## Abstract (arXiv)

Research on multilingual speech emotion recognition faces the problem that most available speech corpora differ from each other in important ways, such as annotation methods or interaction scenarios. These inconsistencies complicate building a multilingual system. We present results for cross-lingual and multilingual emotion recognition on English and French speech data with similar characteristics in terms of interaction (human-human conversations). Further, we explore the possibility of fine-tuning a pre-trained cross-lingual model with only a small number of samples from the target language, which is of great interest for low-resource languages. To gain more insights in what is learned by the deployed convolutional neural network, we perform an analysis on the attention mechanism inside the network.