A method for predicting the intelligibility of noisy and non-linearly enhanced binaural speech
Asger Heidemann Andersen, Jan Mark de Haan, Zheng-Hua Tan, Jesper Jensen
Abstract
We propose and evaluate a binaural speech intelligibility measure. The measure is a binaural extension of the Short-Time Objective Intelligibility (STOI) measure and focuses on predicting the intelligibility of noisy speech which has been enhanced by a speech processing algorithm (e.g. in a hearing aid). We show that the measure can accurately predict 1) the Speech Reception Threshold (SRT) for a frontal speaker masked by a point noise source in the horizontal plane, 2) the improvement in SRT obtained by independently processing the left and right ear signals with Ideal Time Frequency Segregation (ITFS), and 3) the intelligibility of speech in the presence of multiple interferers as well as the effect of processing the noisy signals with 2-microphone MVDR beamforming as used in hearing aids. Finally, we show that the computational demands associated with the measure are favourable in comparison with those of a previously proposed measure with similar properties.
BibTeX
@inproceedings{icassp2016_amethodforpredic,
title = {A method for predicting the intelligibility of noisy and non-linearly enhanced binaural speech},
author = {Asger Heidemann Andersen and Jan Mark de Haan and Zheng-Hua Tan and Jesper Jensen},
booktitle = {ICASSP 2016},
year = {2016}
}