Distributional Learning of Vowel Categories Is Supported by Prosody in Infant-Directed Speech

Distributional Learning of Vowel Categories is Supported by Prosody in Infant-Directed Speech Frans Adriaans (adriaans@psych.upenn.edu) and Daniel Swingley (swingley@psych.upenn.edu) Department of Psychology and Institute for Research in Cognitive Science, University of Pennsylvania 3401 Walnut Street, Suite 400A, Philadelphia, PA 19104, USA Abstract have used approximations of infant-directed speech tokens as input to computational procedures (such as multivariate Gaussian mixture models) that succeed in learning vowel cat- egories, suggesting that distributional learning could be fea- sible for infants (de Boer & Kuhl, 2003; McMurray, Aslin, & Toscano, 2009; Vallabha, McClelland, Pons, Werker, & Amano, 2007). Infants’ acquisition of phonetic categories involves a dis- tributional learning mechanism that operates on acoustic dimensions of the input. However, natural infant-directed speech shows large degrees of phonetic variability, and the resulting overlap between categories suggests that category learning based on distributional clustering may not be feasible without constraints on the learning process, or exploitation of other sources of information. The present study examines whether mothers’ prosodic modifications within infant- directed speech help the distributional learning of vowel categories. Specifically, we hypothesize that ‘motherese’ provides the infant with a subset of high-quality learning tokens that improve category learning. In an analysis of vowel tokens taken from natural mother-infant interactions, we found that prosody can be used to distinguish high-quality tokens (with expanded formant frequencies) from low-quality tokens in the input. Moreover, in simulations of distributional learning we found that models trained on this small set of high-quality tokens provide better classification than models trained on the complete set of tokens. Taken together, these findings show that distributional learning of vowel categories can be improved by attributing importance to tokens that are prosodically prominent in the input. The prosodic properties of motherese might thus be a helpful cue for infants in supporting phonetic category learning. Some caution is appropriate in interpreting these findings. Studies that show the usefulness of distributional cues for category learning have, in large part, been based on analy- ses (and simulations) of vowel tokens that were elicited in a laboratory setting, and that occurred in a small number of words or nonwords. It is possible that maternal speech under these conditions is different from maternal speech in quotidian home contexts. Analyses of natural, unscripted infant-directed speech recordings show that vowel distribu- tions are highly variable, and that overlap between categories poses a substantial problem for distributional category learn- ing (Swingley, 2009). One possibility suggested by this result is that infants’ learning of phonetic categories is guided by ad- ditional sources of information, such as the emerging lexicon (Feldman, Griffiths, & Morgan, 2009; Swingley, 2009). Keywords: Infant-directed speech; phonetic category learning; prosody; computational modeling. Another possibility, explored here, is that infants are able to succeed in category learning because they have a bias to attend to some tokens more than to others, and that these salient tokens are clearer instances of their categories. If so, the difficulty of distributional category learning is overesti- mated by considering the whole mass of experienced speech sounds. This notion is indirectly supported by studies show- ing that infants prefer “motherese” speech over adult-directed speech. Across different languages motherese is character- ized by acoustic exaggeration, including higher overall pitch, greater intonation contours, and longer durations (Fernald et al., 1989; Grieser & Kuhl, 1988; Kuhl et al., 1997). These properties have been found to modulate infants’ attention, and possibly facilitate language learning by enhancing infants’ speech discrimination skills (Fernald & Kuhl, 1987; Karzon, 1985; Liu, Kuhl, & Tsao, 2003; Trainor & Desjardins, 2002). Introduction Infants in the first year of life develop knowledge of the phonetic categories that make up the consonants and vow- els of their native language (e.g., Werker & Tees, 1984). The early age at which this takes place rules out learning accounts in which semantic contrast in phonologically sim- ilar words drives most category learning. As a result, it is assumed that infants learn phonetic categories using an im- plicit statistical clustering process that relies on separation of the categories in perceptual space. Indeed, 6- and 8-month- old infants have been found to form representations of two distinct categories (e.g., /d/ and /t/) when exposed to an artificially generated bimodal distribution on a distinguish- ing acoustic dimension, but not when exposed to a unimodal distribution (Maye, Werker, & Gerken, 2002; Maye, Weiss, & Aslin, 2008; see also Cristi`a, McGuire, Seidl, & Francis, 2011). Further evidence for the plausibility of distributional learning of phonetic category structure comes from analyses of infant-directed speech. Mothers appear to provide their in- fants with acoustic cues that support distributional learning of phonetic categories (Werker et al., 2007). In particular, infant-directed speech is characterized by expansion of the F1-F2 vowel formant space, which could enhance the separa- bility of vowel categories (Kuhl et al., 1997). Several studies It remains to be demonstrated that motherese effectively guides the infant’s attention to those vowel tokens that are most useful for category learning. Computational models that aim to explain category learning are typically fit to isolated, equally weighted vowel tokens (de Boer & Kuhl, 2003; Val- labha et al., 2007). Such models overlook prosodic context which might make certain vowel tokens more attractive than others, and which thus potentially affects the learnability of vowel categories.