Assessment of hearing aid algorithms using a master hearing aid: the influence of hearing aid experience on the relationship between speech recognition and cognitive capacity

Abstract

Objectives: Model-based hearing aid development considers the assessment of speech recognition using a master hearing aid (MHA). It is known that aided speech recognition in noise is related to cognitive factors such as working memory capacity (WMC). This relationship might be mediated by hearing aid experience (HAE). The aim of this study was to examine the relationship of WMC and speech recognition with a MHA for listeners with different HAE. Design: Using the MHA, unaided and aided 80% speech recognition thresholds in noise were determined. Individual WMC capacity was assed using the Verbal Learning and Memory Test (VLMT) and the Reading Span Test (RST). Study sample: Forty-nine hearing aid users with mild to moderate sensorineural hearing loss divided into three groups differing in HAE. Results: Whereas unaided speech recognition did not show a significant relationship with WMC, a significant correlation could be observed between WMC and aided speech recognition. However, this only applied to listeners with HAE of up to approximately three years, and a consistent weakening of the correlation could be observed with more experience. Conclusions: Speech recognition scores obtained in acute experiments with an MHA are less influenced by individual cognitive capacity when experienced HA users are taken into account.

Key Words:

• Model-based algorithms
• master hearing aid
• speech recognition
• working memory

Acknowledgements

This work was supported by a grant of the German Ministry of Education and Research (BMBF), under project 01EZ1127C (“Model-based Hearing Aids”).

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

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