Understanding speech in noisy environments is a challenge for many people with hearing loss.

Traditional speech-in-noise (SiN) tests often focus only on whether words are correctly recognized.

The Repeat-Recall Test (RRT) was developed to provide a more complete picture by assessing not only speech understanding, but also the cognitive effort, memory demands, and listening experience associated with everyday communication.

We combined the RRT with the ezSRT, a Bayesian adaptive testing method that efficiently determines an individual’s speech-reception threshold (SRT).

RRT materials have been adopted for use in a Bayesian-guided adaptive SiN protocol, known as the “easy” speech reception threshold (ezSRT) test.

The ezSRT is designed to efficiently estimate a listener’s complete psychometric function for different SiN conditions.

This innovation enabled the creation of the new protocol called the intelligibility-based Repeat-Recall Test (i-RRT), allowing listeners to be tested at personalized listening conditions corresponding to specific levels of speech understanding (25%, 50%, 75%, and 90% word recognition).

i-RRT incorporates normative references, derived from data from listeners with normal hearing, for each outcome measure at multiple SRT levels.

These benchmarks were then used to evaluate listeners with hearing loss.

The results showed that factors such as the availability of semantic context, the type of background noise, and hearing status influence memory performance, listening effort, and motivation, even when speech intelligibility is held constant.

These results highlight that speech-in-noise difficulties cannot always be explained by audibility alone.

By comparing individual performance against intelligibility-matched normative references, the i-RRT offers a new way to identify hearing-related challenges that may persist even when speech understanding improves.

This approach could ultimately support more personalized assessment and management of real-world communication difficulties.