Automatic Breathing Pattern Analysis from Reading-Speech Signals

As the speech production mechanism is related to the breathing process, speech signals and breathing patterns impact each other. Breathing patterns are the physiological signals which help in understanding the psychological, physiological and cognitive states of an individual. Capturing such patterns relies on the availability of equipment such as respiratory belts, which are costly and uncomfortable to wear for long duration. In this paper, we attempt to extract the breathing patterns from speech signals, which are easily available and can be recorded using a smartphone's microphone. In the presented work, simultaneous speech and breath signals are captured from 100 Indians of the age group 20 to 25 years while they read a phonetically balanced passage in English language. We have identified five distinct breathing templates; following two broad speech-breath categories, exhibited by the speakers while they read the same passage. For one of the two categories, the time domain features with regression network can extract the breathing patterns from speech with a Pearson correlation coefficient of 0.70. By computational modelling, we distinguish these two breathing categories from speech with a classification accuracy of 79%.