Open AccessBehavioral Validation of the Smartphone for Remote Microphone Technology
Author(s) -
Stephanie Tittle,
Linda Thibodeau,
Issa Panahi,
Serkan Tokgoz,
Nikhil Shankar,
Gautam Shreedhar Bhat,
Kashyap Patel
Publication year - 2020
Publication title -
seminars in hearing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.592
H-Index - 27
eISSN - 1098-8955
pISSN - 0734-0451
DOI - 10.1055/s-0040-1718714
Subject(s) - active listening , microphone , smartphone app , audiology , noise (video) , speech recognition , computer science , task (project management) , smartphone application , hearing aid , hearing impaired , psychology , multimedia , medicine , human–computer interaction , communication , telecommunications , engineering , artificial intelligence , sound pressure , systems engineering , image (mathematics)
As part of a National Institutes of Health-National Institute on Deafness and Other communication Disorders (NIH-NIDCD)-supported project to develop open-source research and smartphone-based apps for enhancing speech recognition in noise, an app called Smartphone Hearing Aid Research Project Version 2 (SHARP-2) was tested with persons with normal and impaired hearing when using three sets of hearing aids (HAs) with wireless connectivity to an iPhone. Participants were asked to type sentences presented from a speaker in front of them while hearing noise from behind in two conditions, HA alone and HA + SHARP-2 app running on the iPhone. The signal was presented at a constant level of 65 dBA and the signal-to-noise ratio varied from -10 to +10, so that the task was difficult when listening through the bilateral HAs alone. This was important to allow for improvement to be measured when the HAs were connected to the SHARP-2 app on the smartphone. Benefit was achieved for most listeners with all three manufacturer HAs with the greatest improvements recorded for persons with normal (33.56%) and impaired hearing (22.21%) when using the SHARP-2 app with one manufacturer's made-for-all phones HAs. These results support the continued development of smartphone-based apps as an economical solution for enhancing speech recognition in noise for both persons with normal and impaired hearing.