Publications

Several modification algorithms that alter natural or synthetic speech with the goal of improving intelligibility in noise have been proposed recently. A key requirement of many modification techniques is the ability to predict intelligibility, both offline during algorithm development, and online, in order to determine the optimal modification for the current noise context. While existing objective intelligibility metrics (OIMs) have good predictive power for unmodified natural speech in stationary and fluctuating noise, little is known about their effectiveness for other forms of speech. The current study evaluated how well seven OIMs predict listener responses in three large datasets of