A speech intelligibility index-based approach to predict the speech reception threshold for sentences in fluctuating noise for normal-hearing listeners

The SII model in its present form (ANSI S3.5-1997, American National Standards Institute, New York) can accurately describe intelligibility for speech in stationary noise but fails to do so for nonstationary noise maskers. Here, an extension to the SII model is proposed with the aim to predict the s...

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Bibliographic Details
Published in:The Journal of the Acoustical Society of America 2005-04, Vol.117 (4), p.2181-2192
Main Authors: RHEBERGEN, Koenraad S, VERSFELD, Niek J
Format: Article
Language:English
Subjects:
Acoustics
Attention
Audition
Auditory Threshold
Biological and medical sciences
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
Fundamental areas of phenomenology (including applications)
Humans
Mathematical Computing
Noise
Perception
Perceptual Masking
Physics
Psychoacoustics
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Reference Values
Signal Processing, Computer-Assisted
Sound Localization
Sound Spectrography
Speech Perception
Speech Reception Threshold Test
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Summary:The SII model in its present form (ANSI S3.5-1997, American National Standards Institute, New York) can accurately describe intelligibility for speech in stationary noise but fails to do so for nonstationary noise maskers. Here, an extension to the SII model is proposed with the aim to predict the speech intelligibility in both stationary and fluctuating noise. The basic principle of the present approach is that both speech and noise signal are partitioned into small time frames. Within each time frame the conventional SII is determined, yielding the speech information available to the listener at that time frame. Next, the SII values of these time frames are averaged, resulting in the SII for that particular condition. Using speech reception threshold (SRT) data from the literature, the extension to the present SII model can give a good account for SRTs in stationary noise, fluctuating speech noise, interrupted noise, and multiple-talker noise. The predictions for sinusoidally intensity modulated (SIM) noise and real speech or speech-like maskers are better than with the original SII model, but are still not accurate. For the latter type of maskers, informational masking may play a role.
ISSN:0001-4966
1520-8524
DOI:10.1121/1.1861713