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

TL;DR

This study evaluates the generalization of speech emotion recognition models for Urdu across multiple datasets using domain-knowledge acoustic features, highlighting the importance of cross-corpus validation for low-resource languages.

Contribution

It introduces a cross-corpus evaluation framework for Urdu SER and demonstrates the limitations of self-corpus validation in assessing model robustness.

Findings

Cross-corpus evaluation yields lower UAR than self-corpus validation by up to 13%.

Domain-knowledge acoustic features effectively represent speech signals for Urdu SER.

Cross-corpus validation provides a more realistic assessment of model performance.

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.