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Representation learning with parameterised quantum circuits for advancing speech emotion recognition
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DOI：10.1038/s41598-025-27871-4.png)
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Abstract
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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. Furthermore, Adjusted Rand Index (ARI) analysis demonstrates that the quantum model yields feature representations with improved alignment to true emotion classes compared with the classical model, reinforcing the observed performance gains. 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.
Keywords:
Quantum machine learning
Deep learning
Speech emotion recognition
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