The Band Importance Function in the Evaluation of the Speech Intelligibility Index at the Speech Reception Threshold within a Simulated Driving Environment

Author(s):  
Nikolina Samardzic ◽  
Colin Novak
2021 ◽  
Vol 69 (2) ◽  
pp. 173-179
Author(s):  
Nilolina Samardzic ◽  
Brian C.J. Moore

Traditional methods for predicting the intelligibility of speech in the presence of noise inside a vehicle, such as the Articulation Index (AI), the Speech Intelligibility Index (SII), and the Speech Transmission Index (STI), are not accurate, probably because they do not take binaural listening into account; the signals reaching the two ears can differ markedly depending on the positions of the talker and listener. We propose a new method for predicting the intelligibility of speech in a vehicle, based on the ratio of the binaural loudness of the speech to the binaural loudness of the noise, each calculated using the method specified in ISO 532-2 (2017). The method was found to give accurate predictions of the speech reception threshold (SRT) measured under a variety of conditions and for different positions of the talker and listener in a car. The typical error in the predicted SRT was 1.3 dB, which is markedly smaller than estimated using the SII and STI (2.0 dB and 2.1 dB, respectively).


2017 ◽  
Vol 28 (02) ◽  
pp. 119-126 ◽  
Author(s):  
In-Ki Jin ◽  
James M. Kates ◽  
Kathryn H. Arehart

Background: Graphical methods for calculating the speech intelligibility index (SII), such as the count-the-dot audiogram, are useful tools in quantifying how much weighted audibility is restored when amplification is used for individuals with hearing loss. The band-importance function (BIF), which is an important component of the SII, depends on the language. Thus, language may affect the prediction of weighted audibility using the graphical SII. Purpose: The purpose of this study was to apply language-specific BIFs to develop and compare graphical SIIs for English, Korean, and Mandarin. Research Design: The graphical SIIs were developed and compared using a research design that applied and analyzed existing datasets. Data Collection and Analysis: Language-specific BIFs and dynamic ranges were used to derive graphical SIIs for English, Korean, and Mandarin. SII predictions were compared by calculating the language-specific predictions for the same audiometric configurations. Results: The graphical SIIs for English, Korean, and Mandarin yielded different unaided and aided predictions for the same audiogram configurations. Conclusions: A graphical SII helps patients easily understand their weighted audibility for unaided and aided conditions; thus, it is a useful counseling tool in the clinic. The most accurate graphical SII’s will, however, be based on a patient’s spoken language.


2019 ◽  
Vol 62 (5) ◽  
pp. 1517-1531 ◽  
Author(s):  
Sungmin Lee ◽  
Lisa Lucks Mendel ◽  
Gavin M. Bidelman

Purpose Although the speech intelligibility index (SII) has been widely applied in the field of audiology and other related areas, application of this metric to cochlear implants (CIs) has yet to be investigated. In this study, SIIs for CI users were calculated to investigate whether the SII could be an effective tool for predicting speech perception performance in a population with CI. Method Fifteen pre- and postlingually deafened adults with CI participated. Speech recognition scores were measured using the AzBio sentence lists. CI users also completed questionnaires and performed psychoacoustic (spectral and temporal resolution) and cognitive function (digit span) tests. Obtained SIIs were compared with predicted SIIs using a transfer function curve. Correlation and regression analyses were conducted on perceptual and demographic predictor variables to investigate the association between these factors and speech perception performance. Result Because of the considerably poor hearing and large individual variability in performance, the SII did not predict speech performance for this CI group using the traditional calculation. However, new SII models were developed incorporating predictive factors, which improved the accuracy of SII predictions in listeners with CI. Conclusion Conventional SII models are not appropriate for predicting speech perception scores for CI users. Demographic variables (aided audibility and duration of deafness) and perceptual–cognitive skills (gap detection and auditory digit span outcomes) are needed to improve the use of the SII for listeners with CI. Future studies are needed to improve our CI-corrected SII model by considering additional predictive factors. Supplemental Material https://doi.org/10.23641/asha.8057003


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