Bypassing Optimization Complexity through Transfer Learning & Deep Neural Nets for Speech Intelligibility Improvement

Abstract

This extended abstract highlights the research ventures and findings in the domain of speech intelligibility improvement. Till this point, an effort has been to simulate the Lombard effect, which is the deliberate human attempt to make a speech more intelligible when speaking in the presence of interfering background noise. To that end, an attempt has been made to shift the formants away from the noisy regions in spectrum both sub-optimally and optimally. The sub-optimal shifting methods were based upon Kalman filtering and EM approach. The optimal shifting involved the use of optimization to maximize an objective intelligibility index after shifting the formants. A transfer learning framework was also set up to bring down the computational complexity.

Tasks

Reproductions