论文标题
使用瞬时插语呼吸功能的扬声器和姿势分类
Speaker and Posture Classification using Instantaneous Intraspeech Breathing Features
论文作者
论文摘要
从语音中提取的声学特征被广泛用于诸如生物识别识别和第一人称活动检测等问题。但是，将语音用于此目的会引发隐私问题，因为加工方可以访问内容。在这项工作中，我们提出了一种使用插语呼吸声的扬声器和姿势分类的方法。使用Hilbert-Huang Transform（HHT）提取瞬时幅度特征，并馈入CNN-GRU网络，以分类我们为这项研究收集的开放式静脉内呼吸声数据集（我们收集的）。使用呼吸呼吸声，获得87％的扬声器分类和98％的姿势分类精度。
Acoustic features extracted from speech are widely used in problems such as biometric speaker identification and first-person activity detection. However, the use of speech for such purposes raises privacy issues as the content is accessible to the processing party. In this work, we propose a method for speaker and posture classification using intraspeech breathing sounds. Instantaneous magnitude features are extracted using the Hilbert-Huang transform (HHT) and fed into a CNN-GRU network for classification of recordings from the open intraspeech breathing sound dataset, BreathBase, that we collected for this study. Using intraspeech breathing sounds, 87% speaker classification, and 98% posture classification accuracy were obtained.
