Study of a Signal-processing Algorithm Aiming at Improving Speech-in-noise Intelligibility in Normal-hearing and Hearing-impaired Persons

Nearly half a billion people suffer from disabling hearing loss. The most common form of hearing loss in adults is age-related hearing loss (ARHL), which causes a reduced ability to understand speech in noisy environments. The ability of people with ARHL to communicate is therefore greatly impacted, limiting their social interactions and thus their quality of life. Yet, the wear of hearing aids - which is the current standard rehabilitation treatment in such cases - does not lead to optimal satisfactory outcomes when it comes to understanding speech in noisy environments. The objective of this pilot study is to test a new signal-processing algorithm, based on artificial intelligence, that aims at enhancing the intelligibility of speech-in-noise signals. The efficiency of the algorithm is compared to a standard denoising algorithm commonly used in hearing aids. The primary outcome measure is the word-identification performance of the participants, using the FrMatrix test (Jansen et al., 2012). Two secondary outcome measures are investigated: listening effort (self-assessed using a Likert scale, and measured through response times), and subjective preference (assessed in a paired-comparison task). The study is conducted in 20 normal-hearing subjects and in 40 older (age ≥ 55 years) hearing-impaired subjects.