Transient Noise Reduction Using a Deep Recurrent Neural Network: Effects on Subjective Speech Intelligibility and Listening Comfort.
Mahmoud Keshavarzi, Tobias Reichenbach, Brian C J Moore
PMID 34606381WHAT IT FOUND
People with hearing loss preferred a deep learning algorithm for reducing transient noises over both no processing and a standard algorithm.
They found it made speech clearer and more comfortable, though the difference in clarity compared to the standard method was small.
Key findings
01Participants with hearing loss significantly preferred the RNN processing over the no-processing condition for both subjective speech intelligibility and listening comfort.
02For participants with hearing loss, the RNN was significantly preferred over the MCTR algorithm for listening comfort, but the preference for intelligibility over MCTR was not statistically significant.
03Objective measures showed the RNN resulted in higher estimated speech intelligibility scores compared to both the no-processing and MCTR conditions.
STILL TO COME
How it was doneWhat they foundWhat it means for SLPs
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What it does not show
The study used only ten participants in each group, which limits the statistical power and generalisability of the findings. Participants reported subjective preferences rather than performing objective speech recognition tests, so actual intelligibility was not measured. The transient sounds were added to speech in quiet; the algorithm's performance in complex background noise was not tested. The processing delay of the RNN might be too long for open-fit hearing aid fittings.
Declared interests
The authors declared no competing financial interests. The study was supported by non-U.S. government funding.
The easy way to misread this
Do not assume this algorithm will significantly improve your patients' ability to understand speech in real-world noisy environments. The study only tested transient sounds in quiet, and the preference for better intelligibility over the standard algorithm was not statistically significant for the hearing-impaired group.