Project Summary/Abstract
The presence of background noise often has a substantial, negative impact on a listener’s ability to understand
speech. Unfortunately, deciphering speech in noisy environments is commonplace for listeners. As our recent
preliminary data demonstrate, when the speech signal itself is degraded, such as due to the neurological
speech disorder of dysarthria, the addition of background noise only further decreases a listener’s ability to
understand that speech (Yoho & Borrie, 2018). A highly promising technique developed to remediate much of
this speech-in-noise difficulty is termed the Ideal Binary Mask (IBM). The IBM, and IBM estimation, has
demonstrated significant improvements in understanding speech in noise for listeners with sensorineural
hearing loss. However, research on this technique to date has focused on healthy, intact speech. The
proposed research involves an innovative application of the IBM—to overcome the speech-in-noise difficulties
with degraded speech. This application is important for listeners with either normal hearing or hearing loss, as
even normal hearing listeners struggle considerably to understand degraded speech in noise. This proposal
aims to demonstrate proof-of-concept for the application of IBM to a degraded speech signal, starting with the
test case of dysarthria. In Aim 1, we quantify the ability of the IBM to restore intelligibility of dysarthric speech in
noise to performance levels in quiet. Comparisons will be made between conditions of speech in noise, speech
in noise processed by the IBM, and baseline speech in quiet, for both healthy and dysarthric speech. A
predictive model will quantify the benefit of IBM processing for normal-hearing listeners, and possible
moderators on this benefit. In Aim 2, we examine the benefit achieved from the IBM for listeners with
sensorineural hearing impairment. Data analysis will follow that of Aim 1, and model outcomes will be
compared to those from Aim 1. Successful completion of this project will inform the development of a R01
proposal evaluating the effects of IBM processing on a wide range of severities and types of degraded or
imperfect speech, and account for cognitive processes such as listening effort. Knowledge of how IBM
processing works with an imperfect speech signal will support the refinement of current IBM-estimation
algorithms, optimizing functionality in realistic listening environments. The possible impact of this proposed
research is profound—IBM-based processing has the potential not only to improve the lives of listeners with
hearing loss, but also the conversational partners of speakers with dysarthria and other speech disorders. The
long-term goal of this work is to provide clinically-significant tools to transform treatment of these two important
populations—individuals with speech disorders and individuals with hearing loss. This collaborative research
plan is the first proof of concept step to demonstrate several possible novel applications of this promising
technique for new clinical treatments.