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BioMed Research International
Volume 2015 (2015), Article ID 932519, 8 pages
Research Article

Spoken Word Recognition Errors in Speech Audiometry: A Measure of Hearing Performance?

1Department of Language, Literature and Communication, Language and Hearing Center Amsterdam, VU Free University Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, Netherlands
2The Eargroup, Herentalsebaan 75, 2100 Antwerpen, Belgium

Received 19 January 2015; Revised 1 June 2015; Accepted 8 June 2015

Academic Editor: Markus Hess

Copyright © 2015 Martine Coene et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This report provides a detailed analysis of incorrect responses from an open-set spoken word-repetition task which is part of a Dutch speech audiometric test battery. Single-consonant confusions were analyzed from 230 normal hearing participants in terms of the probability of choice of a particular response on the basis of acoustic-phonetic, lexical, and frequency variables. The results indicate that consonant confusions are better predicted by lexical knowledge than by acoustic properties of the stimulus word. A detailed analysis of the transmission of phonetic features indicates that “voicing” is best preserved whereas “manner of articulation” yields most perception errors. As consonant confusion matrices are often used to determine the degree and type of a patient’s hearing impairment, to predict a patient’s gain in hearing performance with hearing devices and to optimize the device settings in view of maximum output, the observed findings are highly relevant for the audiological practice. Based on our findings, speech audiometric outcomes provide a combined auditory-linguistic profile of the patient. The use of confusion matrices might therefore not be the method best suited to measure hearing performance. Ideally, they should be complemented by other listening task types that are known to have less linguistic bias, such as phonemic discrimination.