  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.
