Infants learn their native language(s) at an amazing speed. Before they even talk, their perception adapts to the language(s) they hear. However, the mechanisms responsible for this and the circumstances in which it takes place remain unclear. This paper presents the first attempt to study perceptual attunement using ecological child-centered audio data. We show that a simple prediction algorithm exhibits perceptual attunement when applied on unrealistic clean audio-book data, but fails to do so when applied on ecologically-valid child-centered data. In the latter scenario, perceptual attunement only emerges when the prediction mechanism is supplemented with inductive biases that force the algorithm to focus exclusively on speech segments while learning speaker-, pitch-, and room-invariant representations. We argue these biases are plausible given previous research on infants and non-human animals. More generally, we show that our model learns and it develops through exposure to speech depends exquisitely on the details of the input signal. By doing so, we illustrate the importance of considering ecologically valid input data when modeling language acquisition.

中文翻译：

---

从以儿童为中心的音频数据中模拟早期语音习得

婴儿以惊人的速度学习母语。在他们说话之前，他们的感知就会适应他们听到的语言。然而，造成这种情况的机制以及发生的情况仍不清楚。本文首次尝试使用以生态儿童为中心的音频数据来研究感知协调。我们证明，一种简单的预测算法在应用于不切实际的干净有声读物数据时表现出感知协调，但在应用于生态有效的以儿童为中心的数据时却无法做到这一点。在后一种情况下，只有当预测机制辅以归纳偏差时，感知协调才会出现，这些偏差迫使算法在学习说话人、音高和房间不变表示的同时只关注语音片段。我们认为，鉴于之前对婴儿和非人类动物的研究，这些偏见是合理的。更一般地说，我们表明我们的模型通过接触语音进行学习和发展，很大程度上取决于输入信号的细节。通过这样做，我们说明了在建模语言习得时考虑生态有效的输入数据的重要性。