Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings

Respiration is an essential and primary mechanism for speech production. We first inhale then produce while exhaling. When we run out of breath, stop speaking inhale. Though this process involuntary, production involves a systematic outflow air during exhalation characterized by linguistic content prosodic factors the utterance. Thus respiration are closely related, modeling relationship makes sensing respiratory dynamics directly from plausible, however not well explored. In article, conduct comprehensive study to explore techniques breathing signal parameters using deep learning architectures address challenges involved in establishing practical purpose technology. Estimating pattern would give us information about parameters, thus enabling understand health one’s speech.