A prominent hypothesis holds that by speaking to infants in infant-directed speech (IDS) as opposed to adult-directed speech (ADS), parents help them learn phonetic categories. Specifically, two characteristics of IDS have been claimed to facilitate learning: hyperarticulation, which makes the categories more separable, and variability, which makes the generalization more robust. Here, we test the separability and robustness of vowel category learning on acoustic representations of speech uttered by Japanese adults in ADS, IDS (addressed to 18- to 24-month olds), or read speech (RS). Separability is determined by means of a distance measure computed between the five short vowel categories of Japanese, while robustness is assessed by testing the ability of six different machine learning algorithms trained to classify vowels to generalize on stimuli spoken by a novel speaker in ADS. Using two different speech representations, we find that hyperarticulated speech, in the case of RS, can yield better separability, and that increased between-speaker variability in ADS can yield, for some algorithms, more robust categories. However, these conclusions do not apply to IDS, which turned out to yield neither more separable nor more robust categories compared to ADS inputs. We discuss the usefulness of machine learning algorithms run on real data to test hypotheses about the functional role of IDS.

中文翻译：

---

婴儿定向语音有助于语音学习吗？机器学习调查

一个突出的假设认为，通过以婴儿定向语音 (IDS) 而非成人定向语音 (ADS) 与婴儿交谈，父母可以帮助他们学习语音类别。具体来说，IDS 的两个特征被认为有助于学习：hyperarticulation，这使得类别更可分离，以及可变性，这使得泛化更健壮. 在这里，我们测试了元音类别学习对日本成年人在 ADS、IDS（针对 18 至 24 个月大的儿童）或朗读语音 (RS) 中发出的语音的声学表征的可分离性和稳健性。可分离性是通过计算五个日语短元音类别之间的距离度量来确定的，而稳健性是通过测试六种不同的机器学习算法训练来对元音进行分类以概括 ADS 中的新说话者所说的刺激的能力来评估的。使用两种不同的语音表示，我们发现在 RS 的情况下，超清晰语音可以产生更好的可分离性，并且 ADS 中增加的说话人之间的可变性可以为某些算法产生更稳健的类别。但是，这些结论不适用于 IDS，结果证明，与 ADS 输入相比，它既不会产生更可分离的类别，也不会产生更强大的类别。我们讨论了机器学习算法在真实数据上运行以测试有关 IDS 功能作用的假设的有用性。