量化用于医疗应用的语音的卷积神经网络鲁棒性
Quantifying Quanvolutional Neural Networks Robustness for Speech in Healthcare Applications
AI总结：
本文研究了QNN在医疗语音应用中的鲁棒性，发现其在音高、时间及速度变化下表现优于CNN，但对高斯噪声仍较敏感，且收敛速度更快。
摘要：
基于语音的机器学习系统对噪声敏感，这使得在情绪识别和语音病理检测中的可靠部署变得复杂。我们评估了混合量子机器学习模型，即quanvolutional神经网络（QNNs）相对于经典卷积神经网络（CNNs）在四种声学失真（高斯噪声、音高变化、时间变化和速度变化）下的鲁棒性，采用干净训练/失真测试的模式。使用AVFAD（语音病理）和TESS（语音情绪），我们比较了三种QNN模型（随机、基本、强）与一个简单的CNN基线（CNN-Base）、ResNet-18和VGG-16，使用准确率和失真度量（CE、mCE、RCE、RmCE），并分析了架构因素（电路复杂度或深度、收敛性）以及每种情绪的鲁棒性。QNNs在音高变化、时间变化和速度变化下普遍优于CNN-Base（在严重时间变化下CE/RCE低至22%），而CNN-Base在高斯噪声下更具鲁棒性。在量子电路中，QNN-Basic在AVFAD上实现了最佳的鲁棒性，而QNN-Random在TESS上表现最强。情绪方面，恐惧在严重失真下最鲁棒（80-90%的准确率），中性在强高斯噪声下可能崩溃（5.5%的准确率），而快乐最易受音高、时间变化和速度失真影响。QNNs的收敛速度比CNN-Base快六倍。据我们所知，这是一次系统研究了在常见非对抗性声学失真下QNN对语音的鲁棒性，表明浅层纠缠量子前端可以提高噪声韧性，但对加性噪声的敏感性仍是一个挑战。
英文摘要：
Speech-based machine learning systems are sensitive to noise, complicating reliable deployment in emotion recognition and voice pathology detection. We evaluate the robustness of a hybrid quantum machine learning model, quanvolutional neural networks (QNNs) against classical convolutional neural networks (CNNs) under four acoustic corruptions (Gaussian noise, pitch shift, temporal shift, and speed variation) in a clean-train/corrupted-test regime. Using AVFAD (voice pathology) and TESS (speech emotion), we compare three QNN models (Random, Basic, Strongly) to a simple CNN baseline (CNN-Base), ResNet-18 and VGG-16 using accuracy and corruption metrics (CE, mCE, RCE, RmCE), and analyze architectural factors (circuit complexity or depth, convergence) alongside per-emotion robustness. QNNs generally outperform the CNN-Base under pitch shift, temporal shift, and speed variation (up to 22% lower CE/RCE at severe temporal shift), while the CNN-Base remains more resilient to Gaussian noise. Among quantum circuits, QNN-Basic achieves the best overall robustness on AVFAD, and QNN-Random performs strongest on TESS. Emotion-wise, fear is most robust (80-90% accuracy under severe corruptions), neutral can collapse under strong Gaussian noise (5.5% accuracy), and happy is most vulnerable to pitch, temporal, and speed distortions. QNNs also converge up to six times faster than the CNN-Base. To our knowledge, this is a systematic study of QNN robustness for speech under common non-adversarial acoustic corruptions, indicating that shallow entangling quantum front-ends can improve noise resilience while sensitivity to additive noise remains a challenge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
