We investigate whether infant‐directed speech (IDS) could facilitate word form learning when compared to adult‐directed speech (ADS). To study this, we examine the distribution of word forms at two levels, acoustic and phonological, using a large database of spontaneous speech in Japanese. At the acoustic level we show that, as has been documented before for phonemes, the realizations of words are more variable and less discriminable inIDSthan inADS. At the phonological level, we find an effect in the opposite direction: TheIDSlexicon (...) contains more distinctive words (such as onomatopoeias) than theADScounterpart. Combining the acoustic and phonological metrics together in a global discriminability score reveals that the bigger separation of lexical categories in the phonological space does not compensate for the opposite effect observed at the acoustic level. As a result,IDSword forms are still globally less discriminable thanADSword forms, even though the effect is numerically small. We discuss the implication of these findings for the view that the functional role ofIDSis to improve language learnability. (shrink)

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. (shrink)