Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition
Abstract
Quantum machine learning (QML) offers a promising avenue for advancing representation learning in complex signal domains. In this study, we investigate the use of parameterised quantum circuits (PQCs) for speech emotion recognition (SER) a challenging task due to the subtle temporal variations and overlapping affective states in vocal signals. We propose a hybrid quantum classical architecture that integrates PQCs into a conventional convolutional neural network (CNN), leveraging quantum properties such as superposition and entanglement to enrich emotional feature representations. Experimental evaluations on three benchmark datasets IEMOCAP, RECOLA, and MSP-IMPROV demonstrate that our hybrid model achieves improved classification performance relative to a purely classical CNN baseline, with over 50% reduction in trainable parameters. This work provides early evidence of the potential for QML to enhance emotion recognition and lays the foundation for future quantum-enabled affective computing systems.
Cite
@article{arxiv.2501.12050,
title = {Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition},
author = {Thejan Rajapakshe and Rajib Rana and Farina Riaz and Sara Khalifa and Björn W. Schuller},
journal= {arXiv preprint arXiv:2501.12050},
year = {2025}
}