Speech-in-noise perception is a major problem for users of cochlear
implants (CIs), especially with non-stationary background noise. Noise-reduction
algorithms have produced benefits but relied on a priori
information about the target speaker and/or background noise. We developed a
recurrent neural network (RNN) algorithm for enhancing speech in non-stationary
noise and evaluated its benefits for speech perception, using both objective
measures and experiments with CI simulations and CI users. The RNN was trained
using speech from many talkers mixed with multi-talker or traffic noise
recordings. Its performance was evaluated using speech from a novel talker mixed
with novel noise recordings of the same class, either babble or traffic noise.
Objective measures indicated benefits of using a recurrent over a feed-forward
architecture and predicted better speech intelligibility with than without the
processing. The experimental results showed significantly improved
intelligibility of speech in babble noise but not in traffic noise. CI subjects
rated the processed stimuli as significantly better in terms of speech
distortions, noise intrusiveness and overall quality than unprocessed stimuli
for both babble and traffic noise. These results extend previous findings for CI
users to mostly unseen acoustic conditions with non-stationary noise.
Cortical tracking of a background speaker modulates the comprehension of a foreground speech signal Abbreviated title: Cortical tracking of background speech
Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using recordings of the output of the two microphones of a behind-the-ear hearing aid in response to male and female speech at various azimuths in the presence of noise produced by wind from various azimuths with a velocity of 3 m/s, using the “clean” speech as a reference. A paired-comparison procedure was used to compare all possible combinations of three conditions for subjective intelligibility and for sound quality or comfort. The conditions were unprocessed noisy speech, noisy speech processed using the RNN, and noisy speech that was high-pass filtered (which also reduced wind noise). Eighteen native English-speaking participants were tested, nine with normal hearing and nine with mild-to-moderate hearing impairment. Frequency-dependent linear amplification was provided for the latter. Processing using the RNN was significantly preferred over no processing by both subject groups for both subjective intelligibility and sound quality, although the magnitude of the preferences was small. High-pass filtering (HPF) was not significantly preferred over no processing. Although RNN was significantly preferred over HPF only for sound quality for the hearing-impaired participants, for the results as a whole, there was a preference for RNN over HPF. Overall, the results suggest that reduction of wind noise using an RNN is possible and might have beneficial effects when used in hearing aids.
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