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arXiv · 2501.12050

Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition

Thejan Rajapakshe·Rajib Rana·Farina Riaz·Sara Khalifa·Björn W. Schuller
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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.

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Thejan Rajapakshe, Rajib Rana, Farina Riaz, Sara Khalifa, Björn W. Schuller. 2025-06-25. Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition. https://arxiv.org/abs/2501.12050

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