Children with hearing loss (HL) remain at risk for poorer language abilities than normal hearing (NH) children despite targeted interventions; reasons for these differences remain unclear. In NH children, research suggests speech discrimination is related to language outcomes, yet we know little about it in children with HL under the age of 2 years. We utilized a vowel contrast, /a‐i/, and a consonant‐vowel contrast, /ba‐da/, to examine speech discrimination in 47 NH infants and 40 infants with HL. At Mean age =3 months, EEG recorded from 11 scalp electrodes was used to compute the time‐frequency mismatched response (TF‐MMR_SE) to the contrasts; at Mean age =9 months, behavioral discrimination was assessed using a head turn task. A machine learning (ML) classifier was used to predict behavioral discrimination when given an arbitrary TF‐MMR_SE as input, achieving accuracies of 73% for exact classification and 92% for classification within a distance of one class. Linear fits revealed a robust relationship regardless of hearing status or speech contrast. TF‐MMR_SE responses in the delta (1–3.5 Hz), theta (3.5–8 Hz), and alpha (8–12 Hz) bands explained the most variance in behavioral task performance. Our findings demonstrate the feasibility of using TF‐MMR_SE to predict later behavioral speech discrimination.

中文翻译：

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不匹配反应可预测听力损失和听力正常婴儿的行为言语辨别结果

尽管有针对性的干预措施，听力损失 (HL) 的儿童仍面临比听力正常 (NH) 儿童更差的语言能力风险；这些差异的原因仍不清楚。在 NH 儿童中，研究表明言语歧视与语言结果有关，但我们对 2 岁以下的 HL 儿童知之甚少。我们使用元音对比 /a-i/ 和辅音-元音对比 /ba-da/ 来检查 47 名 NH 婴儿和 40 名 HL 婴儿的言语辨别能力。在平均年龄 = 3 个月时，使用从 11 个头皮电极记录的脑电图来计算时频失配反应（TF-MMR _SE) 对比；在平均年龄 = 9 个月时，使用转头任务评估行为歧视。当给定任意 TF-MMR _SE作为输入时，使用机器学习 (ML) 分类器来预测行为辨别，准确分类的准确率达到 73%，在一类距离内的分类准确率达到 92%。无论听力状态或语音对比度如何，线性拟合都显示出稳健的关系。δ (1-3.5 Hz)、theta (3.5-8 Hz) 和 alpha (8-12 Hz) 波段中的TF-MMR _{SE响应解释了行为任务表现的最大差异。}我们的研究结果证明了使用 TF-MMR _SE来预测后来的行为言语辨别的可行性。