A novel Speech intelligibility improvement method using maximizing Mutual Information measure
Imam Khomeini International University
Tuesday 2 june, 2015, 17:40 - 18:00
0.3 Copenhagen (49)
We propose a novel speech pre-processing algorithm for speech intelligibility improvement in noisy environments. The speech intelligibility improvement algorithms are often employed in Public Address Systems (PAS) where a clean audio message is played by loudspeakers through the public place. The public place could be a train station, stadium or an airport. These algorithms modify the clean signal such that it would be more intelligible for the listener in the presence of additive background noise. Our proposed method uses an Objective Intelligibility Measure (OIM) to obtain optimal parameters for energy redistributing of the clean signal in the sub-band domain under an energy constraint. Recently, it has shown that Mutual Information as an OIM can successfully predict speech intelligibility . Hence, our algorithm maximizes the mutual information between the spectral envelope of the clean and noisy modified speech for energy redistribution. Optimal parameters which are energy gains of different frequency bands are obtained using this maximization procedure. It is also possible to obtain these parameters adaptively in short blocks of speech. We compare our method with a reference method  that uses a perceptual distortion measure for optimally redistributing speech energy over the time and frequency. The obtained intelligibility scores by STOI and CSII measures in 4 different noisy conditions (babble, train, white and factory noises) shows that our proposed algorithm provides significant gain over the unprocessed speech signal and has higher scores in comparison with the reference method.  J. Taghia, R. Martin, and R. C. Hendriks, "On mutual information as a measure of speech intelligibility," ICASSP, 2012, pp. 65–68.  C. H. Taal, R.C. Hendriks, and R. Heusdens, "A speech preprocessing strategy for intelligibility improvement in noise based on a perceptual distortion measure," ICASSP, 2012, pp. 4061–4064.
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