Authors:

GAULTIER, C., GOEHRING, T.

Reference:

Year of publication:

In Press

CBU number:

8985

Abstract:

For cochlear implant (CI) listeners, holding a conversation in noisy and reverberant environ- ments is often challenging. Deep learning algorithms can potentially mitigate these difficul- ties by enhancing speech in everyday listening environments. This study compared several deep learning algorithms with access to one, two unilateral or six bilateral microphones that were trained to recover speech signals by jointly removing noise and reverberation. The noisy-reverberant speech and an ideal noise-reduction algorithm served as lower and upper references. Objective signal metrics were compared with results from two listening tests, including 15 typical hearing listeners with CI simulations and 12 CI listeners. Large and statistically significant improvements in speech reception thresholds of 7.4 and 10.3 dB were found for the multi-microphone algorithms. For the single-microphone algorithm, there was an improvement of 2.3 dB, but only for the CI listener group. The objective signal met- rics correctly predicted the rank order of results for CI listeners, and there was an overall agreement for most effects and variances between results for CI simulations and CI listeners. These algorithms hold promise to improve speech intelligibility for CI listeners in environ- ments with noise and reverberation, and benefit from a boost in performance when using features extracted from multiple microphones.