Non-intrusive speech intelligibility assessment

We present NISI, a novel non-intrusive speech intelligibility assessment method based on feature extraction and a binary tree regression model. A training method using the intrusive STOI method to automatically label large quantities of speech data is presented and utilized. Our method is shown to predict speech intelligibility with an RMS error of 0.08 STOI on a test database of noisy speech.