Predicting Audiovisual Word Recognition in Noisy Situations: Toward Precision Audiology.
Predicting Audiovisual Word Recognition in Noisy Situations: Toward Precision Audiology.
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DOI：
10.1097/aud.0000000000001072
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发表时间：
2021-11-01
期刊：
影响因子：
3.7
通讯作者：
Sommers M
中科院分区：
文献类型：
--
作者：
Myerson J;Tye-Murray N;Spehar B;Hale S;Sommers M
当一个人既能看到说话者又能听到说话者时，口头交流会更好。虽然观察到与年龄相关的言语感知缺陷，但Tye-Murray及其同事发现，视听（AV）表现可以从仅听觉（A-only）和视觉（V-only）表现中准确预测，并且知道个人的年龄并没有增加预测的准确性。这一发现与传统观点相矛盾，根据传统观点，AV语音感知中与年龄相关的差异是由于听觉和视觉信息整合的缺陷，我们的主要目标是确定Tye-Murray等人是否的发现与一个封闭的测试推广到更像那些在日常生活中的情况。第二个目标是测试一种新的预测模型，对听力学评估有重要意义。受试者（N=109;年龄在22-93岁之间），之前由，进行了我们新的开放式词汇表测试，以评估他们对单个单词的听觉，视觉和视听感知。所有测试均在约62 dB SPL的六个说话者（三男三女）中进行。Lex-List项目的音频水平在呈现时约为59 dB SPL，因为试点测试表明，该信噪比将避免AV条件下的天花板性能。多元线性回归分析显示，只有A和V的性能占87.9%的变异AV语音感知，年龄的贡献未能达到显着性。我们的新抛物线模型解释了更多（92.8%）的AV性能的方差，并且年龄的贡献也不显著。贝叶斯分析显示，对于线性和抛物线模型，当前数据在简化模型（无年龄）中发生的可能性几乎是完整模型（以年龄作为预测因子）的10倍。此外，两个简化模型的比较显示，抛物线模型的数据发生的可能性是线性回归模型的100倍以上。目前的研究结果强烈支持假设，AV性能可以准确地预测从单峰性能，知道个人的年龄并不增加预测的准确性。我们的研究结果代表了一个重要的第一步，在扩展Tye-Murray等人。的调查结果，更像那些在日常交流中遇到的情况。在这项研究中，预测言语感知的准确性预示着一种精确的听力学形式，在这种形式中，确定个体的优势和劣势，在单峰和多模态的言语感知有利于识别目标的康复努力，旨在恢复和维持言语感知能力的关键老年人的生活质量。
Spoken communication is better when one can see as well as hear the talker. Although age-related deficits in speech perception were observed, Tye-Murray and colleagues found that audiovisual (AV) performance could be accurately predicted from auditory-only (A-only) and visual-only (V-only) performance, and that knowing individuals’ ages did not increase the accuracy of prediction. This finding contradicts conventional wisdom, according to which age-related differences in AV speech perception are due to deficits in the integration of auditory and visual information, and our primary goal was to determine whether Tye-Murray et al.’s finding with a closed-set test generalizes to situations more like those in everyday life. A second goal was to test a new predictive model that has important implications for audiological assessment. Participants (N=109; ages 22–93 years), previously studied by, were administered our new, open-set Lex-List test to assess their auditory, visual, and audiovisual perception of individual words. All testing was conducted in six-talker babble (three males and three females) presented at approximately 62 dB SPL. The level of the audio for the Lex-List items, when presented, was approximately 59 dB SPL because pilot testing suggested that this signal-to-noise ratio would avoid ceiling performance in the AV condition. Multiple linear regression analyses revealed that A-only and V-only performance accounted for 87.9% of the variance in AV speech perception, and that the contribution of age failed to reach significance. Our new parabolic model accounted for even more (92.8%) of the variance in AV performance, and again, the contribution of age was not significant. Bayesian analyses revealed that for both linear and parabolic models, the present data were almost 10 times as likely to occur with a reduced model (without Age) than with a full model (with Age as a predictor). Furthermore, comparison of the two reduced models revealed that the data were more than 100 times as likely to occur with the parabolic model than with the linear regression model. The present results strongly support hypothesis that AV performance can be accurately predicted from unimodal performance and that knowing individuals’ ages does not increase the accuracy of that prediction. Our results represent an important initial step in extending Tye-Murray et al.’s findings to situations more like those encountered in everyday communication. The accuracy with which speech perception was predicted in this study foreshadows a form of precision audiology in which determining individual strengths and weaknesses in unimodal and multimodal speech perception facilitates identification of targets for rehabilitative efforts aimed at recovering and maintaining speech perception abilities critical to the quality of an older adult’s life.