A context-based approach to predict intelligibility of meaningful and nonsense words in interrupted noise: Model evaluation

Abstract

The context-based Extended Speech Transmission Index (cESTI) by Van Schoonhoven et al. (2022) was successfully used to predict the intelligibility of meaningful, monosyllabic words in interrupted noise. However, it is not clear how the model behaves when using different degrees of context. In the current paper, intelligibility of meaningful and nonsense CVC words in stationary and interrupted noise was measured in fourteen normally hearing adults. Intelligibility of nonsense words in interrupted noise at −18 dB SNR was relatively poor, possibly because listeners did not profit from coarticulatory cues as they did in stationary noise. With 75% of the total variance explained, the cESTI model performed better than the original ESTI model (R2 = 27%), especially due to better predictions at low interruption rates. However, predictions for meaningful word scores were relatively poor (R2 = 38%), mainly due to remaining inaccuracies at interruption rates below 4 Hz and a large effect of forward masking. Adjusting parameters of the forward masking function improved the accuracy of the model to a total explained variance of 83%, while the predicted power of previously published cESTI data remained similar.