Evaluating a Deep Neural Noise-Reduction Algorithm for Hearing Aids

Evaluating a Deep Neural Noise-Reduction Algorithm for Hearing Aids in Varying Signal-to-Noise Conditions

This study is designed to understand how different hearing-aid noise-reduction technologies affect a listener's ability to hear speech in noisy environments. Participants will listen to speech at several background-noise levels while trying different processing settings. By comparing performance across these conditions, the study aims to identify which types of noise reduction improve speech intelligibility the most. We expect that some noise-reduction strategies will help listeners understand speech better than others, especially in more difficult listening situations.

Study Overview

• Status: Recruiting
• Conditions: Hearing Loss, Cochlear
• Intervention / Treatment: Device: Hearing Aid Noise Reduction - Off; Device: Hearing Aid Noise Reduction - Low; Device: Hearing Aid Noise Reduction - High; Other: Negative SNR; Other: Zero signal-to-noise ratio; Other: Positive SNR

Detailed Description

*** STUDY DESCRIPTION ***

Hearing in noisy environments is one of the most common challenges faced by individuals with hearing loss, and even people with normal hearing often struggle to understand speech in situations such as restaurants, classrooms, and busy public spaces. Modern hearing aids use advanced digital signal-processing strategies, especially deep neural network (DNN)-based noise reduction, to improve speech intelligibility in these difficult listening situations. However, these technologies vary widely in how well they work, and their benefits can depend on factors such as noise level, background type, and an individual's degree of hearing loss. This study examines how different noise-reduction strategies affect a listener's ability to understand speech across a range of real-world listening conditions. A monaural, omnidirectional configuration is to isolate single-microphone noise-reduction strategies without the benefit of directional hearing-aid processing. The research compares several processing modes, each representing a distinct noise-reduction algorithm or signal-processing approach. These modes include stronger and weaker forms of noise reduction as well as "off" conditions with no noise-reduction processing applied. Participants will complete listening tasks at several input signal-to-noise ratios (SNRs), representing easier and more difficult levels of background noise. All participants experience each condition in a controlled, fully counterbalanced order to reduce learning effects and bias.

*** OVERVIEW OF THE LISTENING TASKS ***

During the study, listeners complete speech-understanding tasks using the dual-sentence paradigm, a testing method developed to better reflect the real-world cognitive load of listening in noise. Traditional speech tests typically ask a listener to repeat a single sentence at a time. While useful, these single-sentence tasks often underestimate the difficulty of everyday listening, which requires people to monitor, remember, and respond to multiple pieces of information at once. The dual-sentence paradigm addresses this gap by presenting two sentences back-to-back within the same trial. The participant hears Sentence A, followed immediately by Sentence B, spoken by different talkers. The participant is asked to immediately repeat the first sentence and then type the second sentence.

This structure increases cognitive demand by requiring the listener to hold more spoken information in working memory while simultaneously dealing with background noise. The approach provides a more sensitive measure of how signal-processing strategies affect not only audibility but also real-world listening effort, memory load, and speech comprehension.

*** HOW NOISE-REDUCTION STRATEGIES ARE EVALUATED ***

To evaluate the effects of each noise-reduction setting, participants complete the dual-sentence task at several SNRs - for example, easier (positive) SNRs where speech is louder than the noise, and harder (negative) SNRs where noise competes strongly with speech. Each noise-reduction mode is tested at every SNR, producing a full set of performance data for every combination of algorithm strength and noise difficulty. Speech understanding is measured using standard scoring methods for sentence recall tasks. Participants' responses are recorded, and accuracy is scored based on the number of keywords correctly repeated for each sentence. This allows the research team to quantify how different processing strategies influence speech intelligibility and cognitive load under controlled listening conditions.

*** SCIENTIFIC RATIONALE AND EXPECTED CONTRIBUTIONS ***

Deep neural network-based noise-reduction strategies have emerged in many commercial hearing devices, but their performance can vary depending on training data, model complexity, and how aggressively the noise is reduced. Some settings may improve intelligibility but distort the speech signal; others may reduce noise but introduce processing delays or artifacts that affect listener comfort. By systematically comparing multiple noise-reduction algorithms under identical conditions, this study aims to map how different strategies alter both speech understanding and listening difficulty. Findings from this research can help guide the development of more effective, listener-centered noise-reduction approaches for hearing aids. The results may also improve clinical recommendations by identifying which algorithm strengths work best in different noise environments.

*** BROADER SIGNIFICANCE ***

The ability to communicate in noisy surroundings has profound effects on social participation, work performance, and quality of life. Many hearing aid users continue to report difficulty in noisy environments despite technological advancements. A more detailed understanding of how listeners respond to different noise-reduction strategies, especially under the cognitively demanding dual-sentence paradigm, may help manufacturers design more supportive features and may assist clinicians in tailoring fittings to an individual's daily listening needs.

This study ultimately seeks to support future improvements in hearing-aid design and programming by providing objective evidence on how advanced noise-reduction algorithms affect real-world speech understanding.

• Study Type: Interventional
• Enrollment (Estimated): 50
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Indiana
    • West Lafayette, Indiana, United States, 47905
      • Recruiting
      • Purdue University
      • Contact: Joshua Alexander, Ph.D.; Phone Number: 765-494-5956; Email: PurdueEarLab@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• A hearing aid candidate with mild-to-moderate cochlear hearing loss, based on audiometric profile (at least 20 dB of hearing loss at 2000 Hz, with progressively worse hearing levels at higher frequencies).

Exclusion Criteria:

• Normal hearing
• Severe or profound hearing loss
• Conductive hearing loss
• Neural hearing loss

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Randomized
• Interventional Model: Crossover Assignment
• Masking: Double

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Hearing Aid Noise-Reduction Processing; Participants complete all noise-reduction conditions (Off, Low, High) at all tested signal-to-noise ratios in a within-subject crossover design.	Device: Hearing Aid Noise Reduction - Off; No neural noise suppression applied. Baseline processing condition.; Device: Hearing Aid Noise Reduction - Low; Neural noise suppression using the lower-strength algorithm parameters.; Device: Hearing Aid Noise Reduction - High; Neural noise suppression using the higher-strength algorithm parameters.
Experimental: Signal-to-Noise Ratio; Relative speech and noise levels	Other: Negative SNR; Noise levels higher than speech levels; Other: Zero signal-to-noise ratio; Equal speech and noise levels; Other: Positive SNR; Speech levels higher than noise levels

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Immediate Speech Recall	Accuracy of immediate verbal repetition of the first sentence in each dual-sentence trial. Participants hear Sentence A followed by Sentence B; they immediately repeat Sentence A aloud. Performance is scored as the number of keywords correctly recalled per sentence. This measure assesses speech intelligibility under different noise-reduction processing conditions and signal-to-noise ratios.	During a single study visit (approximately 1-2 hours), assessed immediately following presentation of each dual-sentence trial.
Delayed Speech Recall (Cognitive Demand)	Accuracy of delayed recall of the second sentence in each dual-sentence trial. Participants hear Sentence A followed by Sentence B, repeat the first sentence immediately, and then type the second sentence from memory. Performance is scored as the number of keywords correctly recalled per sentence. This measure reflects the combined effects of speech intelligibility, working-memory load, and cognitive demand under each noise-reduction processing condition and signal-to-noise ratio.	During a single study visit (approximately 1-2 hours), assessed immediately following presentation of each dual-sentence trial.

Collaborators and Investigators

• Sponsor: Purdue University
• Collaborators: Oticon

Publications and helpful links

Helpful Links

• Purdue EAR Lab

Study record dates

Study Major Dates

• Study Start (Actual): October 16, 2025
• Primary Completion (Estimated): April 30, 2026
• Study Completion (Estimated): April 30, 2026

Study Registration Dates

• First Submitted: December 3, 2025
• First Submitted That Met QC Criteria: December 12, 2025
• First Posted (Actual): December 17, 2025

Study Record Updates

• Last Update Posted (Actual): December 17, 2025
• Last Update Submitted That Met QC Criteria: December 12, 2025
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Keywords: hearing loss; background noise; hearing aids; speech perception; noise reduction; speech understanding; listening in noise
• Additional Relevant MeSH Terms: Neurologic Manifestations; Nervous System Diseases; Otorhinolaryngologic Diseases; Sensation Disorders; Ear Diseases; Hearing Disorders; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Hearing Loss; Hearing Loss, Sensorineural
• Other Study ID Numbers: 1406014978-2

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Individual trial data, including participant responses, by keyword
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; ICF

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No