mlsanityData to 7 Oct 2026

Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages

arXiv 1812.10411 ↗First posted 15 Dec 2018cs.CL cs.CVcited by 25

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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.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Dec 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 6; 2024: 7; 2025: 8; 2026: 1.

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  1. 2023emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representationcited by 196
  2. 2024EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmarkcited by 39
  3. 2024MEMO: Memory-Guided Diffusion for Expressive Talking Video Generationcited by 38
  4. 2025Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model's Empathycited by 9
  5. 2024SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion Recognitioncited by 6
  6. 2023A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applicationscited by 3
  7. 2024nEMO: Dataset of Emotional Speech in Polishcited by 3
  8. 2024Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised Modelscited by 3
  9. 2023Towards Generalizable SER: Soft Labeling and Data Augmentation for Modeling Temporal Emotion Shifts in Large-Scale Multilingual Speechcited by 2
  10. 2025Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognitioncited by 2
  11. 2025Anatomy-Guided Representation Learning Using a Transformer-Based Network for Thyroid Nodule Segmentation in Ultrasound Imagescited by 2
  12. 2026VoxEmo: Benchmarking Speech Emotion Recognition with Speech LLMscited by 2
13 more
  1. 2021Transfer learning from High-Resource to Low-Resource Language Improves Speech Affect Recognition Classification Accuracycited by 1
  2. 2022Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech datasetcited by 1
  3. 2023Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformerscited by 1
  4. 2023Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languagescited by 1
  5. 2024Are Paralinguistic Representations all that is needed for Speech Emotion Recognition?cited by 1
  6. 2025Complex Swin Transformer for Accelerating Enhanced SMWI Reconstructioncited by 1
  7. 2021Transfer Learning based Speech Affect Recognition in Urducited by 0
  8. 2023SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adultscited by 0
  9. 2024Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimagingcited by 0
  10. 2025Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognitioncited by 0
  11. 2025EmoTale: An Enacted Speech-emotion Dataset in Danishcited by 0
  12. 2025Cross-Corpus Validation of Speech Emotion Recognition in Urdu using Domain-Knowledge Acoustic Featurescited by 0
  13. 2025Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRIcited by 0