Article details

Research area
Speech recognition

Location
Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European

Date
2013

Author(s)
Dushyant Sharma, Patrick Naylor, Mike Brookes

Non-intrusive speech intelligibility assessment

Synopsis:

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.

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