# Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages

arXiv: https://arxiv.org/abs/1812.10411 · HTML: https://mlsanity.com/paper/1812.10411/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2018.

## Built on by (most cited first, 25 of 25)

- [emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation](https://mlsanity.com/paper/2312.15185/) (2023)
- [EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark](https://mlsanity.com/paper/2406.07162/) (2024)
- [MEMO: Memory-Guided Diffusion for Expressive Talking Video Generation](https://mlsanity.com/paper/2412.04448/) (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)
- [SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion Recognition](https://mlsanity.com/paper/2408.07851/) (2024)
- [A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications](https://mlsanity.com/paper/2311.11250/) (2023)
- [nEMO: Dataset of Emotional Speech in Polish](https://mlsanity.com/paper/2404.06292/) (2024)
- [Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised Models](https://mlsanity.com/paper/2409.16920/) (2024)
- [Towards Generalizable SER: Soft Labeling and Data Augmentation for Modeling Temporal Emotion Shifts in Large-Scale Multilingual Speech](https://mlsanity.com/paper/2311.08607/) (2023)
- [Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition](https://mlsanity.com/paper/2506.02059/) (2025)
- [Anatomy-Guided Representation Learning Using a Transformer-Based Network for Thyroid Nodule Segmentation in Ultrasound Images](https://mlsanity.com/paper/2512.12662/) (2025)
- [VoxEmo: Benchmarking Speech Emotion Recognition with Speech LLMs](https://mlsanity.com/paper/2603.08936/) (2026)
- [Transfer learning from High-Resource to Low-Resource Language Improves Speech Affect Recognition Classification Accuracy](https://mlsanity.com/paper/2103.11764/) (2021)
- [Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech dataset](https://mlsanity.com/paper/2211.07307/) (2022)
- [Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers](https://mlsanity.com/paper/2306.13804/) (2023)
- [Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages](https://mlsanity.com/paper/2307.10814/) (2023)
- [Are Paralinguistic Representations all that is needed for Speech Emotion Recognition?](https://mlsanity.com/paper/2402.01579/) (2024)
- [Complex Swin Transformer for Accelerating Enhanced SMWI Reconstruction](https://mlsanity.com/paper/2512.22202/) (2025)
- [Transfer Learning based Speech Affect Recognition in Urdu](https://mlsanity.com/paper/2103.03580/) (2021)
- [SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults](https://mlsanity.com/paper/2311.14483/) (2023)
- [Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging](https://mlsanity.com/paper/2411.10100/) (2024)
- [Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition](https://mlsanity.com/paper/2501.10408/) (2025)
- [EmoTale: An Enacted Speech-emotion Dataset in Danish](https://mlsanity.com/paper/2508.14548/) (2025)
- [Cross-Corpus Validation of Speech Emotion Recognition in Urdu using Domain-Knowledge Acoustic Features](https://mlsanity.com/paper/2510.26823/) (2025)
- [Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI](https://mlsanity.com/paper/2512.20436/) (2025)

## Abstract (arXiv)

Cross-lingual speech emotion recognition is an important task for practical applications. The performance of automatic speech emotion recognition systems degrades in cross-corpus scenarios, particularly in scenarios involving multiple languages or a previously unseen language such as Urdu for which limited or no data is available. In this study, we investigate the problem of cross-lingual emotion recognition for Urdu language and contribute URDU---the first ever spontaneous Urdu-language speech emotion database. Evaluations are performed using three different Western languages against Urdu and experimental results on different possible scenarios suggest various interesting aspects for designing more adaptive emotion recognition system for such limited languages. In results, selecting training instances of multiple languages can deliver comparable results to baseline and augmentation a fraction of testing language data while training can help to boost accuracy for speech emotion recognition. URDU data is publicly available for further research.