Speaker: Karan Nathwani (post-doctoral fellow)
Date: June 9, 2016
In this work, we propose a novel approach aiming at improving the intelligibility of speech in the context of in-car applications. Speech produced in noisy environments is subject to the Lombard effect which gathers a number of voice transformation effects compared to the speech produced in calm environments. To improve intelligibility of in car speech (radio, message alerts, …), we propose to modify the original speech signal by incorporating one of the important Lombard effect, namely the shift of the lower formant center frequencies away from the competing noise regions. The proposed approach exploits traditional Linear Prediction analysis and overlap and add synthesis. We explore several modification strategies and the merit of each modification is evaluated using both objective and subjective tests. It is in particular shown that the improvement of speech intelligibility in car noise is significantly improved for a majority of listeners.