We trained a computational model (the Chunk‐Based Learner; CBL) on a longitudinal corpus of child–caregiver interactions in English to test whether one proposed statistical learning mechanism—backward transitional probability—is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific “chunks” from their speech environment. To test this idea, we trained the model on both recently encountered and cumulative speech input from a longitudinal child language corpus. We then assessed whether the model could accurately reconstruct children's speech. Controlling for utterance length and the presence of duplicate chunks, we found no evidence that the CBL becomes less accurate in its ability to reconstruct children's speech with age.

中文翻译：

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语言输入统计对儿童言语产生影响的建模

我们在英语儿童 - 照顾者互动的纵向语料库上训练了一个计算模型（基于块的学习者；CBL），以测试一种提出的统计学习机制——向后转移概率——是否能够在整个过程中以稳定的准确度预测儿童的言语产生。最初几年的发展。我们预测，随着儿童年龄的增长，该模型对儿童语音生成的重建不太准确，因为儿童逐渐开始使用抽象的形式而不是他们语音环境中的特定“块”来生成语音。为了测试这个想法，我们在最近遇到的和来自纵向儿童语言语料库的累积语音输入上训练了模型。然后我们评估了该模型是否可以准确地重建儿童的语音。