Department of Clinical and Experimental Audiology, Amsterdam University Medical Center, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Department of Otorhinolaryngology and Head & Neck Surgery, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, The Netherlands.
J Acoust Soc Am. 2023 Oct 1;154(4):2476-2488. doi: 10.1121/10.0021302.
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.
范斯科霍芬等人（2022 年）提出的基于语境的扩展言语可懂度指数（cESTI）成功地预测了在中断噪声中具有意义的单音节词的可懂度。然而，目前还不清楚该模型在使用不同程度的语境时的表现如何。在当前的研究中，14 名听力正常的成年人对有意义和无意义的 CVC 词在固定和中断噪声中的可懂度进行了测量。在 -18dB SNR 的中断噪声中，无意义词的可懂度相对较差，这可能是因为与在固定噪声中不同，听众没有从协同发音线索中获益。cESTI 模型以 75%的总方差解释率表现优于原始 ESTI 模型（R2=27%），特别是由于在低中断率下的预测效果更好。然而，对有意义词得分的预测相对较差（R2=38%），主要是由于在中断率低于 4Hz 时仍存在不准确性，以及前向掩蔽的影响较大。调整前向掩蔽函数的参数提高了模型的准确性，总解释方差达到 83%，而先前发表的 cESTI 数据的预测能力仍然相似。
