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

*** 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.