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At birth, infants discriminate most of the sounds of the world’s languages, but by age 1, infants become language-specific listeners. This has generally been taken as evidence that infants have learned which acoustic dimensions are contrastive, or useful for distinguishing among the sounds of their language(s), and have begun focusing primarily on those dimensions when perceiving speech. However, speech is highly variable, with different sounds overlapping substantially in their acoustics, and after decades of research, we still do not know what aspects of the speech signal allow infants to differentiate contrastive from noncontrastive dimensions. Here we show that infants could learn which acoustic dimensions of their language are contrastive, despite the high acoustic variability. Our account is based on the cross-linguistic fact that even sounds that overlap in their acoustics differ in the contexts they occur in. We predict that this should leave a signal that infants can pick up on and show that acoustic distributions indeed vary more by context along contrastive dimensions compared with noncontrastive dimensions. By establishing this difference, we provide a potential answer to how infants learn about sound contrasts, a question whose answer in natural learning environments has remained elusive. 

We incorporate social reasoning about groups of informants into a model of word learning, and show that the model accounts for infant looking behavior in tasks of both word learning and recognition. Simulation 1 models an experiment where 16-month-old infants saw familiar objects labeled either correctly or incorrectly, by either adults or audio talkers. Simulation 2 reinterprets puzzling data from the Switch task, an audiovisual habituation procedure wherein infants are tested on familiarized associations between novel objects and labels. Eight-month-olds outperform 14-month-olds on the Switch task when required to distinguish labels that are minimal pairs (e.g., “buk” and “puk”), but 14-month-olds' performance is improved by habituation stimuli featuring multiple talkers. Our modeling results support the hypothesis that beliefs about knowledgeability and group membership guide infant looking behavior in both tasks. These results show that social and linguistic development interact in non-trivial ways, and that social categorization findings in developmental psychology could have substantial implications for understanding linguistic development in realistic settings where talkers vary according to observable features correlated with social groupings, including linguistic, ethnic, and gendered groups. 

Infants learn the sound categories of their language and adults successfully process the sounds they hear, even though sound categories often overlap in their acoustics. Most researchers agree that listeners use context to disambiguate overlapping categories. However, they differ in their ideas about how context is used. One idea is that listeners normalize out the systematic effects of context from the acoustics of a sound. Another idea is that contextual information may itself be an informative cue to category membership, due to patterns in the types of contexts that particular sounds occur in. We directly contrast these two ways of using context by applying each one to the test case of Japanese vowel length. We find that normalizing out contextual variability from the acoustics does not improve categorization, but using context in a top-down fashion does so substantially. This reveals a limitation of normalization in phonetic acquisition and processing and suggests that approaches that make use of top-down contextual information are promising to pursue. 

We describe a model which jointly performs word segmentation and induces vowel categories from formant values. Vowel induction performance improves slightly over a baseline model which does not segment; segmentation performance decreases slightly from a baseline using entirely symbolic input. Our high joint performance in this idealized setting implies that problems in unsupervised speech recognition reflect the phonetic variability of real speech sounds in context. 

Listeners quickly adapt to novel accents. There are three main hypotheses for how they do so. Some suggest that listeners expand their phonetic categories, allowing more variability in how a sound is pronounced. Others argue that listeners shift their categories instead, only accepting deviations consistent with the accent. A third hypothesis is that listeners both shift and expand their categories. Most work has supported the category expansion hypotheses, with the key exception of Maye et al. (2008) who argued for a shifting strategy. Here, we apply the ideal adaptor model from Kleinschmidt & Jaeger (2015) to reexamine what conclusions can be drawn from their data. We compare adaptation models in which categories are shifted, expanded, or both shifted and expanded. We show that models involving expansion can explain the data as well as, if not better than, the shift model, in contrast to what has been previously concluded from these data.