A noise-robust algorithm for segmentation of breath events during continuous
speech is presented. The built-in microphone of a smartphone is used to capture
the speech signal (voiced and breath frames) under conditions of a relatively
noisy background. A template matching approach, using mel-cepstrograms, is
adopted for constructing several similarity measurements to distinguish between
breath and nonbreath frames. Breath events will be used for lung function
regression.