基于生成对抗网络的半监督学习病理语音分类

Nam H. Trinh, Darragh O'Brien
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引用次数: 3

摘要

深度学习在医学应用中的一个应用是使用深度神经网络将人类语言分类为健康或病理。在这种应用中,音频信号被转换成捕获其时变内容的频谱图,后一种“图像”被送入分类器进行分类。应用这种方法的一个挑战是缺乏适合训练目的的语音数据。标记数据采集需要大量的人力和/或耗时的实验。在本文中,我们提出了一种采用生成对抗网络(GAN)的半监督学习方法来缓解训练数据不足的问题。我们比较了传统分类器和半监督分类器的分类性能。我们观察到,当提供等量的训练样本时,基于gan的半监督方法在准确性和ROC曲线方面表现出显着的改进。
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Semi-Supervised Learning with Generative Adversarial Networks for Pathological Speech Classification
One application of deep learning in medical applications is the use of deep neural networks to classify human speech as healthy or pathological. In such applications, the audio signal is transformed into a spectrogram that captures its time-varying content and the latter “images” are fed into a classifier for classification. A challenge in applying this approach is the shortage of suitable speech data for training purposes. Labelled data acquisition requires significant human effort and/or time-consuming experiments. In this paper, we propose a semi-supervised learning approach that employs a Generative Adversarial Network (GAN) to alleviate the problem of insufficient training data. We compare the classification performance of a traditional classifier and our semi-supervised classifier. We observe that the GAN-based semi-supervised approach demonstrates a significant improvement in terms of accuracy and ROC curve when supplied an equivalent number of training samples.
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