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Quantum Approaches for Dysphonia Assessment in Small Speech Datasets.
Summary
Quantum-based Quanvolutional Neural Networks (QNNs) show superior performance in classifying dysphonia compared to traditional Convolutional Neural Networks (CNNs), especially with limited data.
Area of Science:
- Medical signal processing
- Quantum machine learning
Background:
- Dysphonia, characterized by voice impairments, necessitates accurate diagnostic tools.
- Machine learning, particularly Convolutional Neural Networks (CNNs), shows promise for audio classification but struggles with limited datasets.
- Quantum-based approaches offer potential solutions for data-scarce scenarios.
Purpose of the Study:
- To compare the efficacy of standard CNNs, CNNs with random nonlinear layers (RANDOM models), and hybrid quantum-classical Quanvolutional Neural Networks (QNNs) for dysphonia assessment.
- To evaluate model performance using limited audio speech data.
Main Methods:
- Audio data preprocessed into Mel spectrograms (243 training, 61 testing samples).
- Developed six models: two CNNs, two RANDOM models, and two QNNs, with enhanced versions.
- Utilized angle encoding in the QNN quanvolutional layer for complex feature representation.
Main Results:
- QNN models consistently outperformed CNN models in classification accuracy (82.22%–89.26%) and convergence speed.
- The hybrid quantum-classical approach demonstrated robustness with limited data.
Conclusions:
- Quanvolutional Neural Networks (QNNs) present a promising, high-performing alternative for medical data classification, particularly for conditions like dysphonia.
- Quantum-based machine learning can enhance diagnostic capabilities in data-limited clinical settings.
