The Words-in-Noise (WIN) test with multitalker babble and speech-spectrum noise maskers

from the Journal of the American Academy of Audiology

The Words-in-Noise (WIN) test uses monosyllabic words in seven signal-to-noise ratios of multitalker babble (MTB) to evaluate the ability of individuals to understand speech in background noise. The purpose of this study was to evaluate the criterion validity of the WIN by comparing recognition performances under MTB and speech-spectrum noise (SSN) using listeners with normal hearing and listeners with hearing loss. The MTB and SSN had identical rms and similar spectra but different amplitude-modulation characteristics. The performances by the listeners with normal hearing, which were 2 dB better in MTB than in SSN, were about 10 dB better than the performances by the listeners with hearing loss, which were about 0.5 dB better in MTB with 56% of the listeners better in MTB and 40% better in SSN. The slopes of the functions for the normal-hearing listeners (8-9%/dB) were steeper than the functions for the listeners with hearing loss (5-6%/dB). The data indicate that the WIN has good criterion validity.


About Callier Library

Housed at the internationally renowned Callier Center for Communication Disorders, Callier Library a branch facility of the McDermott Library at The University of Texas at Dallas.

Posted on September 17, 2007, in Uncategorized. Bookmark the permalink. 2 Comments.

  1. Dr. Gregory Delfino CCC-A

    I would like to purchase a copy of the WINT. Please let me know how to obtain a copy.

    Thank you in advance,

    Gregory Delfino Au.D, CCC-A

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )


Connecting to %s

%d bloggers like this: