# Using Electrophysiology to Complement Speech Understanding-in-Noise Measures

> **NIH VA I01** · PORTLAND VA MEDICAL CENTER · 2020 · —

## Abstract

Approximately 37.5 million Americans have some problem hearing, and hearing-related
problems rank as the most common service-connected disabilities. One of the chief complaints
of those with hearing impairment is difficulty communicating in background noise, a challenge
that is exacerbated by several health conditions that are prominent within the Veteran
population (e.g., traumatic brain injury, diabetes, multiple sclerosis, and Parkinson's disease). In
addition, many older individuals have difficulties understanding speech in background noise
beyond what would be expected based solely on their audiometric thresholds. There is a
fundamental need for improved diagnosis and treatment of speech understanding in noise
difficulties. This research program sets out to establish electrophysiological correlates of
speech-in-noise understanding with the goal of supplementing speech-in-noise testing. The
assumption is that accurate perception in noise is dependent, in part, on the accuracy of neural
coding of the auditory stimulus. By combining electrophysiological and behavioral information
we can advance our understanding of perception-in-noise difficulties and predict outcomes in
difficult-to-test individuals using physiological testing.
 The clinical significance of the research proposed is related directly to the ability to
predict, more accurately diagnose, and more precisely treat perception-in-noise difficulties. An
electrophysiological measure that predicts speech perception will allow for improved
assessment of difficult-to-test populations and provide information about the capacity of that
auditory system to encode certain stimuli. This will allow the clinician to tailor treatment
strategies to the specific needs of the individual and to counsel patients more effectively in
terms of the expectations they should have and the benefit they should expect as a result of
specific treatments. Therefore, to further our understanding of signal-in-noise perception and
neural coding, we will use brainstem, cortical, and cognitive auditory evoked potentials and
behavioral speech understanding-in-noise measures with age and hearing impairment as
continuous variables to characterize the effect sizes of various covariates and to improve our
understanding of the relationship between brain and behavioral measures.

## Key facts

- **NIH application ID:** 9906072
- **Project number:** 5I01RX002139-04
- **Recipient organization:** PORTLAND VA MEDICAL CENTER
- **Principal Investigator:** Curtis J Billings
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2017-02-01 → 2021-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9906072

## Citation

> US National Institutes of Health, RePORTER application 9906072, Using Electrophysiology to Complement Speech Understanding-in-Noise Measures (5I01RX002139-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9906072. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*