Application of Ideal Binary Masking to Disordered Speech
Phase: NA
Conditions: Hearing Loss, Sensorineural, Dysarthria
Sponsor: Utah State University

Application of Ideal Binary Masking to Disordered Speech 
NCT05244603 
12/3/2019

Study Protocol 
 
Dysarthria and hearing loss are communication disorders that can substantially reduce 
intelligibility of speech and the addition of background noise adds a further challenge. This 
proposal utilizes an established signal processing technique, currently exploited for improved 
understanding of speech in noise for listeners with hearing loss, to investigate its potential 
application to overcome speech-in-noise difficulties for listeners understanding dysarthric speech. 
Successful completion of this project will demonstrate proof-of-concept for the application of this 
signal processing technique to dysarthric speech in noise, and inform the development of an R01 
proposal to perform a large-scale evaluation of the technology, and clinically meaningful 
implications, in a broad range of disordered speech types and severities. 
 
Dysarthric and healthy control phrases will be processed in six different experimental conditions. 
Conditions 1 and 2 will consist of the unprocessed phrases representing dysarthric (condition 1) 
and control speech (condition 2) in quiet. Conditions 3 and 4 will consist of the phrases 
representing dysarthric (condition 3) and control speech (condition 4) mixed with noise at a 
given signal-to-noise ratio (SNR). For a masker, a cafeteria noise (Auditec of St. Louis) will be 
used to represent a relatively natural listening background. Conditions 5 and 6 will consist of the 
same phrases representing dysarthric (condition 5) and control speech (condition 6) mixed with 
cafeteria noise as in conditions 3 and 4, but processed by the IBM. Speech stimuli will be 
processed by custom MATLAB scripts and will be generated using high-quality D/A converters 
and a PC. 
All participants will be seated in a sound treated booth and fitted with Sennheiser HD280 Pro 
circumaural headphones. Hearing-impaired participants will not wear their hearing aids during 
testing, but audibility will be ensured through the spectral shaping via a digital equalizer. 
Participants will be told that they will be presented with phrases that will be difficult to 
understand, either because the phrases will be produced by a person with a speech disorder 
and/or background noise, and that their job is to listen carefully and try to understand what is 
being said. The experimenter will present the phrases, one at a time, and following each phrase, 
the participants will be asked to repeat back what they think they heard. The experimenter will 
type out the participant’s verbal responses. This method was selected over more traditional 
“hear and type” methods to mitigate possible issues with mobility and typing competencies, 
particularly applicable to the older listeners. Responses will be scored for words correct, using 
previously established scoring criteria for the semantically anomalous phrases and a validated 
in-house computer program.  
Multiple regression analysis will be used to test for differences in PWC scores across the 
conditions. Each model will control for individual age, gender, and PWC scores in quiet—
thereby controlling for baseline perception capabilities.  
 

 
 
Communicative Disorders and Deaf Education    |    comd.usu.edu    |    1000 Old Main Hill    |    Logan, UT 84322 
Page 1 of 2 
 
 
 
Protocol # 10710 
IRB Approval Date: December 3, 2019 
Consent Document Expires: December 2, 2024 
Amendment #6 Approved: October 14, 2021 
 
 
Informed Consent  
 
Application of Ideal Binary Masking to disordered speech 
 
Introduction 
You are invited to participate in a research study conducted by Sarah Yoho Leopold an Assistant Professor in the 
Department of Communicative Disorders and Deaf Education at Utah State University. The purpose of this research 
is to understand how people understand disordered speech in background noise, and whether or not this 
understanding can be improved. Your participation is entirely voluntary. 
 
Procedures 
Your participation will involve listening to speech over headphones and transcribing what was said. The study  will 
last approximately 30 minutes. We anticipate that 400 people will participate in this research study. 
 
Risks 
This is a minimal risk research study. That means that the risks of participating are no more likely or serious than 
those you encounter in everyday activities. The foreseeable risks or discomforts include fatigue due to the length 
of testing (30 minutes). In order to minimize those risks and discomforts, you make take short breaks as needed. 
 
Benefits 
There is no direct benefit to you for participating in this research study. More broadly, this study will help the 
researchers learn more about hearing and speech understanding and may help future individuals with 
communication difficulties through a deeper understanding of how speech understanding can be improved for this 
population. 
 
Confidentiality 
The researchers will make every effort to ensure that the information you provide as part of this study remains 
confidential. Your participation is anonymous. Your identity will not be revealed in any publications, presentations, 
or reports resulting from this research study. 
We will collect your information through assessments and questionnaires. Online activities always carry a risk of 
a data breach, but we will use systems and processes that minimize breach opportunities. Data will be securely 
stored in a restricted-access folder on Box.com, an encrypted, cloud-based storage system and/or in a locked 
drawer in a restricted-access laboratory. No identifiable data will be collected or stored at any time. 
 
Voluntary Participation & Withdrawal 
Your participation in this research is completely voluntary. If you do not complete the study, any data we already 
have from you will be destroyed as soon as possible. 
 
Compensation 
For your participation in this research study, you will receive $4. You will also receive a bonus of $1 if you 
demonstrate engagement in the task by responding the requested tasks. If you choose to withdraw before 
completion of the study, you will not receive compensation. 
 
Findings & Future Participation 
Identifiers may be removed from your data. These de-identified data may be used or distributed for future 
research without additional consent from you. If you do not wish for us to use your data in this way, please state 
so below. 
 
 

 
 
Communicative Disorders and Deaf Education    |    comd.usu.edu    |    1000 Old Main Hill    |    Logan, UT 84322 
Page 2 of 2 
 
 
 
Protocol # 10710 
IRB Approval Date: December 3, 2019 
Consent Document Expires: December 2, 2024 
Amendment #6 Approved: October 14, 2021 
 
 
 
IRB Review 
The Institutional Review Board (IRB) for the protection of human research participants at Utah State University has 
reviewed and approved this study. If you have questions about the research study itself, please contact the Principal 
Investigator at 435-797-9239 or sarah.leopold@usu.edu. If you have questions about your rights or would simply 
like to speak with someone other than the research team about questions or concerns, please contact the IRB 
Director at (435) 797-0567 or irb@usu.edu. 
 
 
 
Sarah Yoho Leopold, PhD  
 Principal Investigator 
(435) 797-9239; sarah.leopold@usu.edu 
 
 
Stephanie Borrie, PhD 
Co-Investigator 
(435) 797-1388; stephanie.borrie@usu.edu 
 
Informed Consent 
By continuing with this study, you agree to participate in this study. You indicate that you understand the risks and 
benefits of participation, and that you know what you will be asked to do. You also agree that you have asked any 
questions you might have and are clear on how to stop your participation in the study if you choose to do so. 
Please be sure to retain a copy of this form for your records. 
  

Statistical Design and Power 
 
The outcome measure for the current study is percent words correct (PWC). To test whether there is a difference 
in PWC between the experimental conditions, linear mixed effects regression analysis will be used. Each mixed 
effects model will control for covariates including individual age, gender, MOCA score, audiometric thresholds, 
frequency-specific audibility of stimuli, and QuickSIN scores. We will also assess PWC scores in quiet as both a 
covariate and as a potential moderator—thereby controlling for baseline perception capabilities—and intra-
individual variability. Of particular interest is whether the IBM-processed speech is significantly more intelligible 
than unprocessed speech in noise (which would indicate that IBM processing provides benefit). These models 
will also test whether IBM-processed speech is not significantly different from unprocessed speech in quiet 
(which would provide evidence that IBM processing restores intelligibility of speech in noise to that of speech in 
quiet). These tests will be done systematically. The first aim includes the unadjusted (bivariate) model as well as 
the adjusted model discussed in the Research Strategy. Aim 2 builds on this by including important moderators. 
 
For power, we used the simr R package to simulate power given the sample size and expected effect sizes. This 
analysis relied on Monte Carlo simulations from a known population model. All code used is provided below. For 
Aim 1, a minimum of 30 participants will be tested per sub-experiment, as this results in sufficient power for the 
planned comparisons (for alpha of .05, with 6 covariates, effect size of .5, power = .93).   
 
For Aim 2, at least 30 participants will be tested, as the power analyses with a moderate interactive effect indicate 
this sample size will result in sufficient power for the planned interactions (for alpha of .05, with 6 covariates, 
power = .91).  
 
The R code to run the analyses are provided below. 
 
 
library(tidyverse) 
set.seed(843) 
 
library(simr) 
 
data_production <- function(npart = 30,          ## number of subjects 
                            nplot = 6,           ## number of repeated measures 
                            effect_x = .5,       ## effect of variable x 
                            sigma_s = 2,         ## variance within participant 
                            sigma = 1) {         ## residual variance 
     x = rep(1:nplot, times = nplot) 
     z = rnorm(npart*nplot) 
     a = rnorm(npart*nplot) 
     b = rnorm(npart*nplot) 
     c = rnorm(npart*nplot) 
     d = rnorm(npart*nplot) 
     e = rnorm(npart*nplot) 
     f = rnorm(npart*nplot) 
     standeff = rep( rnorm(npart, 0, sigma_s), each = nplot) 
     stand = as.character(rep(1:npart, each = nplot)) 
     ploteff = rnorm(npart*nplot, 0, sigma) 
     resp = 1 + effect_x*x + standeff + ploteff 
     dat = data.frame(x = as.character(x), z = scale(z), stand, resp = scale(resp), a, b, c, d, e, f) 
     dat 
} 
 
data_production_int <- function(npart = 30,          ## number of subjects 
                                nplot = 6,           ## number of repeated measures 
                                effect_x = .5,       ## effect of variable x 
                                sigma_s = 2,         ## variance within participant 
                                sigma = 1) {         ## residual variance 
     x = rep(1:nplot, times = nplot) 
     z = rnorm(npart*nplot) 
     a = rnorm(npart*nplot) 
     b = rnorm(npart*nplot) 
     c = rnorm(npart*nplot) 
     d = rnorm(npart*nplot) 
     e = rnorm(npart*nplot) 
     f = rnorm(npart*nplot) 
     standeff = rep( rnorm(npart, 0, sigma_s), each = nplot) 
     stand = as.character(rep(1:npart, each = nplot)) 
     ploteff = rnorm(npart*nplot, 0, sigma) 
     resp = 1 + effect_x*x + effect_x*z + effect_x*x*z + standeff + ploteff 
     dat = data.frame(x = as.character(x), z = scale(z), stand, resp = scale(resp), a, b, c, d, e, f) 

     dat 
} 
 
d <- data_production() 
fit <- lmer(resp ~ x + a + b + c + d + e + f + (1|stand), data = d) 
fit2 <- extend(fit, along="stand", n=30)  
fixef(fit2)[2:3] <- 0.15 
fixef(fit2)[4:5] <- 0.30 
fixef(fit2)[6]   <- 0.45 
# summary(fit2) 
 
broom::augment(fit2) %>%  
  ggplot(aes(factor(x), .fitted, fill = factor(x), color = factor(x))) + 
    geom_boxplot(alpha = .7) + 
    theme_classic() + 
    scale_fill_viridis_d() + 
    scale_color_viridis_d() + 
    theme(legend.position = "none") 
 
sim1 <- powerSim(fit2) 
sim1 
 
d2 <- data_production_int() 
fit <- lmer(resp ~ x*z + a + b + c + d + e + f + (1|stand), data = d2) 
fit2 <- extend(fit, along="stand", n=30)  
fixef(fit2)[14]  <- 0.2 
fixef(fit2)[15]  <- 0.2 
fixef(fit2)[16] <- 0.3 
fixef(fit2)[17] <- 0.3 
fixef(fit2)[18] <- 0.4 
# summary(fit2) 
 
broom::augment(fit2) %>%  
  ggplot(aes(z, .fitted, fill = factor(x), color = factor(x))) + 
    geom_point(alpha = .7) + 
    geom_smooth(method = "lm") + 
    theme_classic() + 
    scale_fill_viridis_d() + 
    scale_color_viridis_d() 
 
sim2 <- powerSim(fit2, test = fcompare(~ x + z)) 
sim2