基于瞬时语音呼吸特征的说话人和说话人分类
Speaker and Posture Classification using Instantaneous Intraspeech Breathing Features
摘要：
从语音中提取的声学特征被广泛应用于生物识别说话人辨识和第一人称活动检测等问题。然而，出于这种目的使用语音会引起隐私问题，因为处理方可以访问内容。在这项工作中，我们提出了一种方法，扬声器和姿势分类使用intraspeech呼吸声。使用希尔伯特-黄变换（HHT）提取瞬时幅度特征，并将其输入CNN-GRU网络，用于对我们为本研究收集的开放式内呼吸声数据集BreathBase的记录进行分类。使用intraspeech呼吸音，87%的扬声器分类，98%的姿势分类准确率。
英文摘要：
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.
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