Individuals with hearing impairment have particular difficulty perceptually segregating concurrent voices and understanding a talker in the presence of a competing voice. In contrast, individuals with normal hearing perform this task quite well. This listening situation represents a very different problem for both the human and machine listener, when compared to perceiving speech in other types of background noise. A machine learning algorithm is introduced here to address this listening situation. A deep neural network was trained to estimate the ideal ratio mask for a male target talker in the presence of a female competing talker. The monaural algorithm was found to produce sentence-intelligibility increases for hearing-impaired (HI) and normal-hearing (NH) listeners at various signal-to-noise ratios (SNRs). This benefit was largest for the HI listeners and averaged 59%-points at the least-favorable SNR, with a maximum of 87%-points. The mean intelligibility achieved by the HI listeners using the algorithm was equivalent to that of young NH listeners without processing, under conditions of identical interference. Possible reasons for the limited ability of HI listeners to perceptually segregate concurrent voices are reviewed as are possible implementation considerations for algorithms like the current one.

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For deep learning based speech segregation to have translational significance as a noise-reduction tool, it must perform in a wide variety of acoustic environments. In the current study, performance was examined when target speech was subjected to interference from a single talker and room reverberation. Conditions were compared in which an algorithm was trained to remove both reverberation and interfering speech, or only interfering speech. A recurrent neural network incorporating bidirectional long short-term memory was trained to estimate the ideal ratio mask corresponding to target speech. Substantial intelligibility improvements were found for hearing-impaired (HI) and normalhearing (NH) listeners across a range of target-to-interferer ratios (TIRs). HI listeners performed better with reverberation removed, whereas NH listeners demonstrated no difference. Algorithm benefit averaged 56 percentage points for the HI listeners at the least-favorable TIR, allowing these listeners to perform numerically better than young NH listeners without processing. The current study highlights the difficulty associated with perceiving speech in reverberant-noisy environments, and it extends the range of environments in which deep learning based speech segregation can be effectively applied. This increasingly wide array of environments includes not only a variety of background noises and interfering speech, but also room reverberation.

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