Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition

TL;DR

This paper explores the integration of parameterised quantum circuits into speech emotion recognition models, demonstrating improved performance and reduced parameters, thus highlighting quantum machine learning's potential in affective computing.

Contribution

It introduces a hybrid quantum-classical architecture for speech emotion recognition that leverages PQCs to enhance feature representation and reduce model complexity.

Findings

Achieved over 50% reduction in trainable parameters.

Improved classification accuracy over classical CNN baseline.

Validated on three benchmark datasets.

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.