PUB - Publikationen an der Universität Bielefeld

Infant-directed speech (IDS) is thought to play a facilitating role in language acquisition, by simplifying the input infants receive. In particular, the hypothesis that the acoustic level is enhanced to make the input more clear for infants, has been extensively studied in the case of vowels, but less so in the case of consonants. An investigation into how nasal consonants can be discriminated in infant- compared to adult-directed speech (ADS) was performed, on a corpus of Japanese mother-infant spontaneous conversations, by examining all bilabial and alveolar nasals occurring in intervocalic position. The Pearson correlation between corresponding spectrum slices of nasal consonants, in identical vowel contexts, was employed as similarity measure and a statistical model was fit using this information. It revealed a decrease in similarity between the nasal classes, in IDS compared to ADS, although the effect was not statistically significant. We confirmed these results, using an unsupervised machine learning algorithm to discriminate between the two nasal classes, obtaining similar classification performance in IDS and ADS. We discuss our findings in the context of the current literature on infant-directed speech.