Effects of low-latency deep-learning-based noise reduction on speech intelligibility for normal hearing, hearing-impaired listeners and cochlear implant users

Objective To investigate the effects of low-latency deep-learning-based noise reduction on measured and predicted speech intelligibility in noise in normal-hearing (NH) listeners, hearing-impaired (HI) listeners and cochlear implant (CI) users. Design A pre-trained convolutional single-channel time-domain audio separation network with ultra-low latency (1 ms) was used to process mixtures of speech from the German matrix sentence test in speech-shaped noise. Speech reception thresholds (SRTs) were measured with and without algorithm processing adaptively aiming at 50% speech understanding. The short-time objective intelligibility measure (STOI) was used for predictions. Study sample Fifty-one adults participated in this study: 20 NH, 19 HI listeners and 12 CI users. Results For NH listeners, the noise reduction algorithm significantly decreased speech intelligibility in noise (median SRT deterioration: 0.9 dB). In contrast, the SRT significantly improved for HI listeners and CI users (0.8 dB and 5.7 dB, respectively). The strong correlation between individual unprocessed SRT and SRT benefit was closely qualitatively predicted using STOI. Maximum SRT benefit was predicted at +5 dB signal-to-noise ratio.