Cross-Corpus Validation of Speech Emotion Recognition in Urdu using Domain-Knowledge Acoustic Features

• URL: http://arxiv.org/abs/2510.26823v1
• Date: Tue, 28 Oct 2025 16:35:48 GMT
• Title: Cross-Corpus Validation of Speech Emotion Recognition in Urdu using Domain-Knowledge Acoustic Features
• Authors: Unzela Talpur, Zafi Sherhan Syed, Muhammad Shehram Shah Syed, Abbas Shah Syed,
• Abstract summary: This study investigates Urdu SER in a cross-corpus setting, an area that has remained largely unexplored.<n>We employ a cross-corpus evaluation framework across three different Urdu emotional speech datasets to test model generalization.<n>Results show that Self-corpus validation often overestimates performance, with UAR exceeding cross-corpus evaluation by up to 13%.
• Score: 0.9449650062296823
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Speech Emotion Recognition (SER) is a key affective computing technology that enables emotionally intelligent artificial intelligence. While SER is challenging in general, it is particularly difficult for low-resource languages such as Urdu. This study investigates Urdu SER in a cross-corpus setting, an area that has remained largely unexplored. We employ a cross-corpus evaluation framework across three different Urdu emotional speech datasets to test model generalization. Two standard domain-knowledge based acoustic feature sets, eGeMAPS and ComParE, are used to represent speech signals as feature vectors which are then passed to Logistic Regression and Multilayer Perceptron classifiers. Classification performance is assessed using unweighted average recall (UAR) whilst considering class-label imbalance. Results show that Self-corpus validation often overestimates performance, with UAR exceeding cross-corpus evaluation by up to 13%, underscoring that cross-corpus evaluation offers a more realistic measure of model robustness. Overall, this work emphasizes the importance of cross-corpus validation for Urdu SER and its implications contribute to advancing affective computing research for underrepresented language communities.

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