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1. Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input

   https://doi.org/10.1073/pnas.2001844118  

   Schatz, Thomas; Feldman, Naomi H.; Goldwater, Sharon; Cao, Xuan-Nga; Dupoux, Emmanuel (January 2021, Proceedings of the National Academy of Sciences)

   null (Ed.)

   Before they even speak, infants become attuned to the sounds of the language(s) they hear, processing native phonetic contrasts more easily than nonnative ones. For example, between 6 to 8 mo and 10 to 12 mo, infants learning American English get better at distinguishing English and [l], as in “rock” vs. “lock,” relative to infants learning Japanese. Influential accounts of this early phonetic learning phenomenon initially proposed that infants group sounds into native vowel- and consonant-like phonetic categories—like and [l] in English—through a statistical clustering mechanism dubbed “distributional learning.” The feasibility of this mechanism for learning phonetic categories has been challenged, however. Here, we demonstrate that a distributional learning algorithm operating on naturalistic speech can predict early phonetic learning, as observed in Japanese and American English infants, suggesting that infants might learn through distributional learning after all. We further show, however, that, contrary to the original distributional learning proposal, our model learns units too brief and too fine-grained acoustically to correspond to phonetic categories. This challenges the influential idea that what infants learn are phonetic categories. More broadly, our work introduces a mechanism-driven approach to the study of early phonetic learning, together with a quantitative modeling framework that can handle realistic input. This allows accounts of early phonetic learning to be linked to concrete, systematic predictions regarding infants’ attunement. 

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2. Evaluating computational models of infant phonetic learning across languages

   Matusevych, Yevgen; Schatz, Thomas; Kamper, Herman; Feldman, Naomi H.; Goldwater, Sharon (July 2020, Proceedings of the Annual Conference of the Cognitive Science Society)

   In the first year of life, infants' speech perception becomes attuned to the sounds of their native language. Many accounts of this early phonetic learning exist, but computational models predicting the attunement patterns observed in infants from the speech input they hear have been lacking. A recent study presented the first such model, drawing on algorithms proposed for unsupervised learning from naturalistic speech, and tested it on a single phone contrast. Here we study five such algorithms, selected for their potential cognitive relevance. We simulate phonetic learning with each algorithm and perform tests on three phone contrasts from different languages, comparing the results to infants' discrimination patterns. The five models display varying degrees of agreement with empirical observations, showing that our approach can help decide between candidate mechanisms for early phonetic learning, and providing insight into which aspects of the models are critical for capturing infants' perceptual development. 

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3. What defines a category? Evidence that listeners’ perception is governed by generalizations

   Richardson, Rachael R.; Feldman, Naomi H.; Idsardi, William (August 2015, Proceedings of the Annual Conference of the Cognitive Science Society)

   Listeners draw on their knowledge of phonetic categories when identifying speech sounds, extracting meaningful structural features from auditory cues. We use a Bayesian model to investigate the extent to which their perceptions of linguistic content incorporate their full knowledge of the phonetic category structure, or only certain aspects of this knowledge. Simulations show that listeners are best modeled as attending primarily to the most salient phonetic feature of a category when interpreting a cue, possibly attending to other features only in cases of high ambiguity. These results support the conclusion that listeners ignore potentially informative correlations in favor of efficient communication. 

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4. Speaker matters: Natural inter-speaker variation affects 4-month-olds’ perception of audio-visual speech

   https://doi.org/10.1177/0142723719876382  

   Pejovic, Jovana; Yee, Eiling; Molnar, Monika (April 2020, First Language)

   In the language development literature, studies often make inferences about infants’ speech perception abilities based on their responses to a single speaker. However, there can be significant natural variability across speakers in how speech is produced (i.e., inter-speaker differences). The current study examined whether inter-speaker differences can affect infants’ ability to detect a mismatch between the auditory and visual components of vowels. Using an eye-tracker, 4.5-month-old infants were tested on auditory-visual (AV) matching for two vowels (/i/ and /u/). Critically, infants were tested with two speakers who naturally differed in how distinctively they articulated the two vowels within and across the categories. Only infants who watched and listened to the speaker whose visual articulations of the two vowels were most distinct from one another were sensitive to AV mismatch. This speaker also produced a visually more distinct /i/ as compared to the other speaker. This finding suggests that infants are sensitive to the distinctiveness of AV information across speakers, and that when making inferences about infants’ perceptual abilities, characteristics of the speaker should be taken into account. 

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5. A quantitative model of the language familiarity effect in infancy

   Thorburn, Craig A.; Feldman, Naomi H.; Schatz, Thomas (September 2019, Proceedings of the Conference on Cognitive Computational Neuroscience)

   Human listeners are better at telling apart speakers of their native language than speakers of other languages, a phenomenon known as the language familiarity effect. The recent observation of such an effect in infants as young as 4.5 months of age (Fecher & Johnson, in press) has led to new difficulties for theories of the effect. On the one hand, retaining classical accounts—which rely on sophisticated knowledge of the native language (Goggin, Thompson, Strube, & Simental, 1991)–requires an explanation of how infants could acquire this knowledge so early. On the other hand, letting go of these accounts requires an explanation of how the effect could arise in the absence of such knowledge. In this paper, we build on algorithms from unsupervised machine learning and zero-resource speech technology to propose, for the first time, a feasible acquisition mechanism for the language familiarity effect in infants. Our results show how, without relying on sophisticated linguistic knowledge, infants could develop a language familiarity effect through statistical modeling at multiple time-scales of the acoustics of the speech signal to which they are exposed. 

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