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Vibro-Tactile Enhancement of Speech Intelligibility in Multi-talker Noise for Simulated Cochlear Implant Listening

Overview of attention for article published in Trends in Hearing, September 2018
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Title
Vibro-Tactile Enhancement of Speech Intelligibility in Multi-talker Noise for Simulated Cochlear Implant Listening
Published in
Trends in Hearing, September 2018
DOI 10.1177/2331216518797838
Pubmed ID
Authors

Mark D. Fletcher, Sean R. Mills, Tobias Goehring

Abstract

Many cochlear implant (CI) users achieve excellent speech understanding in acoustically quiet conditions but most perform poorly in the presence of background noise. An important contributor to this poor speech-in-noise performance is the limited transmission of low-frequency sound information through CIs. Recent work has suggested that tactile presentation of this low-frequency sound information could be used to improve speech-in-noise performance for CI users. Building on this work, we investigated whether vibro-tactile stimulation can improve speech intelligibility in multi-talker noise. The signal used for tactile stimulation was derived from the speech-in-noise using a computationally inexpensive algorithm. Eight normal-hearing participants listened to CI simulated speech-in-noise both with and without concurrent tactile stimulation of their fingertip. Participants' speech recognition performance was assessed before and after a training regime, which took place over 3 consecutive days and totaled around 30 min of exposure to CI-simulated speech-in-noise with concurrent tactile stimulation. Tactile stimulation was found to improve the intelligibility of speech in multi-talker noise, and this improvement was found to increase in size after training. Presentation of such tactile stimulation could be achieved by a compact, portable device and offer an inexpensive and noninvasive means for improving speech-in-noise performance in CI users.

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Geographical breakdown

Country Count As %
Unknown 82 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 20%
Student > Master 12 15%
Researcher 6 7%
Student > Bachelor 5 6%
Student > Postgraduate 5 6%
Other 14 17%
Unknown 24 29%
Readers by discipline Count As %
Engineering 12 15%
Medicine and Dentistry 10 12%
Psychology 10 12%
Neuroscience 7 9%
Computer Science 3 4%
Other 12 15%
Unknown 28 34%