Improved tactile speech robustness to background noise with a dual-path recurrent neural network noise-reduction strategy

Abstract

Abstract Many hearing-impaired people struggle to understand speech in background noise, making noise robustness critical for hearing-assistive devices. Recently developed haptic hearing aids, which convert audio to vibration, can improve speech-in-noise performance for cochlear implant (CI) users and assist those unable to access hearing-assistive devices. Haptic hearing aids are typically worn on the body rather than the head, allowing additional space for batteries and microprocessors. This means they can deploy more sophisticated noise-reduction techniques. In the current study, we assessed whether a real-time-feasible noise-reduction strategy, using a dual-path recurrent neural network (DPRNN), improves the noise robustness of haptic hearing aids. Audio was converted to vibration on the wrist using a previously developed vocoder method, either with or without noise reduction. In 16 participants, tactile-only sentence identification was measured for speech in quiet and in multi-talker background noise. The DPRNN improved sentence identification in noise by 8.2% on average and did not affect performance in quiet. This suggests that advanced techniques like the DPRNN could substantially improve outcomes with haptic hearing aids. Low cost, non-invasive haptic devices could soon be an important supplement to hearing-assistive devices such as CIs or offer an alternative for people who are unable to access CI technology.