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Development and evaluation of a mixed gender, multi-talker matrix sentence test in Australian English

Objective: To develop, in Australian English, the first mixed-gender, multi-talker matrix sentence test. Design: Speech material consisted of a 50-word base matrix whose elements can be combined to form sentences of identical syntax but unpredictable content. Ten voices (five female and five male) w...

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Bibliographic Details
Published in:International journal of audiology 2017-02, Vol.56 (2), p.85-91
Main Authors: Kelly, Heather, Lin, Gaven, Sankaran, Narayan, Xia, Jing, Kalluri, Sridhar, Carlile, Simon
Format: Article
Language:English
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Summary:Objective: To develop, in Australian English, the first mixed-gender, multi-talker matrix sentence test. Design: Speech material consisted of a 50-word base matrix whose elements can be combined to form sentences of identical syntax but unpredictable content. Ten voices (five female and five male) were recorded for editing and preliminary level equalization. Elements were presented as single-talker sentences-in-noise during two perceptual tests: an optimization phase that provided the basis for further level correction, and an evaluation phase that perceptually validated those changes. Study sample: Ten listeners participated in the optimization phase; these and an additional 32 naïve listeners completed the evaluation test. All were fluent in English and all but one had lived in Australia for >2 years. Results: Optimization reduced the standard deviation (SD) and speech reception threshold (SRT) range across all speech material (grand mean SRT = −10.6 dB signal-to-noise ratio, median = −10.8, SD =1.4, range =13.7, slope =19.3%/dB), yielding data consistent with cross-validated matrix tests in other languages. Intelligibility differences between experienced and naïve listeners were minimal. Conclusions: The Australian matrix corpus provides a robust set of test materials suitable for both clinical assessment and research into the dynamics of active listening in multi-talker environments.
ISSN:1499-2027
1708-8186
DOI:10.1080/14992027.2016.1236415