A novel person identification (PID) technique is developed in this study, which exploits a new biometric called bronchial breath sound and speech signal acquired by a stethoscope. In addition to investigating the acoustic characteristics of breath sounds for PID, we evaluate three identification methods, including support vector machines (SVM), artificial neural networks (ANN), and i-vector approach. Recognizing the requirement that the amount of sound data collected from each person should be as small as possible, this work studies data augmentation (DA) techniques that avoid the system training process from the overfitting problem when the training sound data is insufficient. In addition, we apply feature engineering techniques to find the informative subset of breath sound features which is beneficial for PID. Our experiments were conducted using a dataset composed of 16 subjects, including an equal number of male and female participants. In the test phase, both Support Vector Machine combined with feature selection and Artificial Neural Networks approaches yielded the promising accuracies of 98%.

中文翻译：

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听诊器感知的语音和呼吸音用于使用稀疏训练数据进行人员识别

本研究开发了一种新的人员识别 (PID) 技术，该技术利用了一种称为支气管呼吸音和由听诊器获取的语音信号的新生物特征。除了研究 PID 呼吸音的声学特性外，我们还评估了三种识别方法，包括支持向量机 (SVM)、人工神经网络 (ANN) 和 i 向量方法。认识到从每个人收集的声音数据量应尽可能少的要求，这项工作研究了数据增强 (DA) 技术，该技术可在训练声音数据不足时避免系统训练过程中出现过拟合问题。此外，我们应用特征工程技术来找到对 PID 有益的呼吸音特征的信息子集。我们的实验是使用由 16 名受试者组成的数据集进行的，其中包括相同数量的男性和女性参与者。在测试阶段，支持向量机结合特征选择和人工神经网络方法产生了 98% 的有希望的准确率。