ARTICLE Nallanthighal_NEURALNETWORKS_2021/IDIAP Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings Nallanthighal, Venkata Srikanth Mostaani, Zohreh Härmä, Aki Strik, Helmer Magimai.-Doss, Mathew deep neural networks Respiratory parameters signal processing Speech breathing Speech technology Neural Networks 141 211--224 0893-6080 2021 https://doi.org/10.1016/j.neunet.2021.03.029 doi Respiration is an essential and primary mechanism for speech production. We first inhale and then produce speech while exhaling. When we run out of breath, we stop speaking and inhale. Though this process is involuntary, speech production involves a systematic outflow of air during exhalation characterized by linguistic content and prosodic factors of the utterance. Thus speech and respiration are closely related, and modeling this relationship makes sensing respiratory dynamics directly from the speech plausible, however is not well explored. In this article, we conduct a comprehensive study to explore techniques for sensing breathing signal and breathing parameters from speech using deep learning architectures and address the challenges involved in establishing the practical purpose of this technology. Estimating the breathing pattern from the speech would give us information about the respiratory parameters, thus enabling us to understand the respiratory health using one’s speech.