Prediction of the Intelligibility for Speech in Real-Life Background Noises for Subjects With Normal Hearing
Posted by Callier Library on March 6, 2008
from Ear and Hearing
Objectives: The speech reception threshold (SRT) traditionally is measured in stationary noise that has the long-term average speech spectrum of the target speech. However, in real life the instantaneous spectrum of the background noise is likely to be different from the stationary long-term average speech spectrum noise. To gain more insight into the effect of real-life background noises on speech intelligibility, the SRT of listeners with normal hearing was measured in a set of noises that varied in both the spectral and the temporal domain. This article investigates the ability of the extended speech intelligibility index (ESII), proposed by Rhebergen et al. to account for SRTs in these real-life background noises.
Design: SRTs in noise were measured in 12 subjects with normal hearing. Interfering noises consisted of a variety of real-life noises, selected from a database, and chosen on the basis of their spectro-temporal differences. Measured SRTs were converted to ESII values and compared. Ideally, at threshold, ESII values should be the same, because the ESII represents the amount of speech information available to the listener.
Results: SRTs ranged from -6 dB SNR (in stationary noise) to -21 dB SNR (in machine gun noise). Conversion to ESII values resulted in an average value of 0.34, with a standard deviation of 0.06. SRT predictions with the ESII model were better than those obtained with the conventional SII (ANSI S3.5-1997) model. In case of interfering speech, the ESII model predictions were poorer, because additional, nonenergetic (informational) masking is thought to occur.
Conclusions: For the present set of masking noises, being representative for a variety of real-life noises, the ESII model of Rhebergen et al. is able to predict the SRTs of subjects with normal hearing with reasonable accuracy. It may be concluded that the ESII model can provide valuable predictions for the speech intelligibility in some everyday situations.
