用参数化量子电路进行表示学习,以推进语音情感识别和语言识别
Thejan Rajapakshe1, Rajib Rana2, Farina Riaz3
1University of Southern Queensland, Darling Heights, Australia. Thejan.Rajapakshe@unisq.edu.au.
Scientific reports
|November 22, 2025
概括
量子机器学习通过在混合模型中使用量子电路来增强语音情感识别. 与经典方法相比,这种方法提高了准确性,并减少了模型的复杂性.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 信号处理 信号处理
- 情感计算是一种情感计算.
背景情况:
- 语音情感识别 (SER) 由于微妙的声音变化而具有挑战性.
- 复杂信号中的表示学习需要先进的技术.
- 量子机器学习 (QML) 显示了改善信号处理任务的潜力.
研究的目的:
- 研究用于SER的参数化量子电路 (PQC) 的使用.
- 开发一种混合量子经典架构,用于情感识别.
- 评估QML在增强情感特征表示中的有效性.
主要方法:
- 提出了一个混合模型,将PQC与卷积神经网络 (CNN) 集成在一起.
- 利用诸如叠加和纠等量子性质进行特征丰富.
- 在基准数据集上进行的实验:IEMOCAP,RECOLA和MSP-IMPROV.
主要成果:
- 混合量子-经典模型在分类性能方面表现优于纯粹的经典CNN基线.
- 与经典CNN相比,可训练参数减少了50%以上.
- 调整后的兰德指数 (ARI) 分析显示,量子衍生特征与真情感类的调整得到了改善.
结论:
- QML显示了增强情绪识别系统的潜力.
- 混合方法为SER提供了更好的性能和效率.
- 这项研究为未来量子增强的情感计算系统奠定了基础.
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