Quantifying Quanvolutional Neural Networks Robustness for Speech in Healthcare Applications

TL;DR

This study evaluates the robustness of quantum neural networks versus classical CNNs in speech emotion and pathology detection under various acoustic corruptions, showing quantum models often outperform classical ones in robustness and convergence speed.

Contribution

It provides the first systematic comparison of quantum and classical neural networks' robustness to common speech corruptions, highlighting quantum advantages in resilience and training efficiency.

Findings

QNNs outperform CNN-Base under pitch shift, temporal shift, and speed variation.

QNNs converge up to six times faster than CNN-Base.

Emotion-wise, fear is most robust, while neutral and happy are more vulnerable.

Abstract

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.