Quality conversion of non-acoustic signals for facilitating human-to-human speech communication under harsh acoustic conditions

Harsh acoustic conditions limit the effectiveness of human speech communication to a great extent. There is a consensus that even at moderate SNR levels, traditional speech enhancement techniques tend to improve the perceptual quality of speech rather than its intelligibility. As an alternative, non-acoustic contact sensors have recently been developed for noise-robust signal capture. Although relatively immune to ambient noise, due to alternative pickup location and non-acoustic principle of operation, signals measured from these sensors are of lower speech quality and intelligibility when compared to those obtained from a conventional microphone in clean conditions. To facilitate human-to-human speech communication under acoustically adverse environments, in this study we present and evaluate a probabilistic transformation framework to improve perceptual quality and intelligibility of signals acquired from one such sensor entitled: physiological microphone (PMIC). Results from both objective and subjective tests confirm that incorporating this framework as a post-processing stage yields significant improvement in overall quality and intelligibility of PMIC signals.