Understanding speech in the presence of noise with hearing aids can be challenging. Here we describe our entry, submission E003, to the 2021 Clarity Enhancement Challenge Round 1 (CEC1), a machine learning challenge for improving hearing aid processing. We apply and evaluate a deep neural network speech enhancement model with a low-latency recursive least squares (RLS) adaptive beamformer, and a linear equalizer, to improve speech intelligibility in the presence of speech or noise interferers. The enhancement network is trained only on the CEC1 data, and all processing obeys the 5~ms latency requirement. We quantify the improvement using the CEC1 provided hearing loss model and Modified Binaural Short-Time Objective Intelligibility (MBSTOI) score (ranging from 0 to 1, higher being better). On the CEC1 test set, we achieve a mean of 0.644 and median of 0.652 compared to the 0.310 mean and 0.314 median for the baseline. In the CEC1 subjective listener intelligibility assessment, for scenes with noise interferers, we achieve the second highest improvement in intelligibility from 33.2% to 85.5%, but for speech interferers, we see more mixed results, potentially from listener confusion.
Cite as: Yang, S., Wisdom, S., Gnegy, C., Lyon, R.F., Savla, S. (2022) Listening with Googlears: Low-Latency Neural Multiframe Beamforming and Equalization for Hearing Aids. Proc. Interspeech 2022, 3939-3943, doi: 10.21437/Interspeech.2022-10783
@inproceedings{yang22u_interspeech,
author={Samuel Yang and Scott Wisdom and Chet Gnegy and Richard F. Lyon and Sagar Savla},
title={{Listening with Googlears: Low-Latency Neural Multiframe Beamforming and Equalization for Hearing Aids}},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={3939--3943},
doi={10.21437/Interspeech.2022-10783}
}