Speaker and Posture Classification using Instantaneous Intraspeech Breathing Features

• URL: http://arxiv.org/abs/2005.12230v1
• Date: Mon, 25 May 2020 17:00:26 GMT
• Title: Speaker and Posture Classification using Instantaneous Intraspeech Breathing Features
• Authors: At{\i}l \.Ilerialkan, Alptekin Temizel, H\"useyin Hac{\i}habibo\u{g}lu
• Abstract summary: We propose a method for speaker and posture classification using intraspeech breathing sounds. Using intraspeech breathing sounds, 87% speaker classification, and 98% posture classification accuracy were obtained.
• Score: 2.578242050187029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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