Features extracted from speech are widely used for problems such as biometric speaker identification, but the use of speech data raises concerns about privacy. We propose a method for speech and posture classification using only breath data. The acoustical information was extracted from breath instances using the Hilbert-Huang transform and fed into our CNN-RNN network for classification. We also created our publicly available dataset, BreathBase, which contains more than 5000 breath instances of 20 participants in 5 different postures with 4 different microphones. Using this data, 85% speaker classification and 98% posture classification accuracy is obtained.
Date: 27.11.2019 / 15.00 Place: A-212
