用量子电路增强语音情感识别特征表示，参数量减少50%以上
Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition
- 将量子电路嵌入卷积神经网络，利用量子叠加与纠缠提取情感特征
- 在三个数据集上性能优于纯经典模型，参数量降低超50%
- 适合对低参数模型和前沿量子计算应用感兴趣的科研人员
量子机器学习为复杂信号领域的表征学习提供了新可能。本文研究参数化量子电路（PQC）在语音情感识别（SER）中的应用，该任务因语音信号中细微的时间变化和重叠情绪状态而具有挑战性。提出一种混合量子-经典架构，将PQC集成至传统卷积神经网络（CNN），利用量子叠加与纠缠等特性丰富情感特征表示。在IEMOCAP、RECOLA和MSP-IMPROV三个基准数据集上的实验表明，该混合模型相较于纯经典CNN基线，在分类性能上有所提升，且可训练参数减少超过50%。本工作为量子机器学习增强情感识别提供了早期证据，并为未来量子赋能的情感计算系统奠定基础。
原文摘要 · Abstract (English)
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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