量子神经网络在小样本语音数据上表现优于传统模型。
Quantum Approaches for Dysphonia Assessment in Small Speech Datasets
- 用量子-经典混合模型处理小规模语音数据
- 量子模型准确率和稳定性均高于传统CNN
- 适合语音医疗诊断中数据稀缺的场景
声音障碍是一种常见病症，表现为失声、嘶哑或言语中断。为评估该病症，研究者结合传统医学手段与机器学习方法。卷积神经网络（CNN）在音频分类和语音识别中表现优异，但在小样本语音数据上面临挑战。本研究对比了CNN与新型量子-经典混合模型（量化解卷积神经网络，QNN）的性能，后者更适用于小数据集。音频数据经预处理生成梅尔频谱图，共243个训练样本和61个测试样本，进行了十次实验。构建了四类模型（两个QNN和两个CNN），其中第二个模型通过增加层数提升性能。结果表明，QNN模型在多数实验中均展现出更高的准确率和更强的稳定性。
原文摘要 · Abstract (English)
Dysphonia, a prevalent medical condition, leads to voice loss, hoarseness, or speech interruptions. To assess it, researchers have been investigating various machine learning techniques alongside traditional medical assessments. Convolutional Neural Networks (CNNs) have gained popularity for their success in audio classification and speech recognition. However, the limited availability of speech data, poses a challenge for CNNs. This study evaluates the performance of CNNs against a novel hybrid quantum-classical approach, Quanvolutional Neural Networks (QNNs), which are well-suited for small datasets. The audio data was preprocessed into Mel spectrograms, comprising 243 training samples and 61 testing samples in total, and used in ten experiments. Four models were developed (two QNNs and two CNNs) with the second models incorporating additional layers to boost performance. The results revealed that QNN models consistently outperformed CNN models in accuracy and stability across most experiments.
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