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1. 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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2. How to use context to disambiguate overlapping categories: The test case of Japanese vowel length

   Hitczenko, Kasia; Mazuka, Reiko; Elsner, Micha; Feldman, Naomi H. (July 2018, Proceedings of the Annual Conference of the Cognitive Science Society)

   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. 

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3. Neural tracking measures of speech intelligibility: Manipulating intelligibility while keeping acoustics unchanged

   https://doi.org/10.1073/pnas.2309166120  

   Karunathilake, I. M.; Kulasingham, Joshua P.; Simon, Jonathan Z. (December 2023, Proceedings of the National Academy of Sciences)

   Neural speech tracking has advanced our understanding of how our brains rapidly map an acoustic speech signal onto linguistic representations and ultimately meaning. It remains unclear, however, how speech intelligibility is related to the corresponding neural responses. Many studies addressing this question vary the level of intelligibility by manipulating the acoustic waveform, but this makes it difficult to cleanly disentangle the effects of intelligibility from underlying acoustical confounds. Here, using magnetoencephalography recordings, we study neural measures of speech intelligibility by manipulating intelligibility while keeping the acoustics strictly unchanged. Acoustically identical degraded speech stimuli (three-band noise-vocoded, ~20 s duration) are presented twice, but the second presentation is preceded by the original (nondegraded) version of the speech. This intermediate priming, which generates a “pop-out” percept, substantially improves the intelligibility of the second degraded speech passage. We investigate how intelligibility and acoustical structure affect acoustic and linguistic neural representations using multivariate temporal response functions (mTRFs). As expected, behavioral results confirm that perceived speech clarity is improved by priming. mTRFs analysis reveals that auditory (speech envelope and envelope onset) neural representations are not affected by priming but only by the acoustics of the stimuli (bottom–up driven). Critically, our findings suggest that segmentation of sounds into words emerges with better speech intelligibility, and most strongly at the later (~400 ms latency) word processing stage, in prefrontal cortex, in line with engagement of top–down mechanisms associated with priming. Taken together, our results show that word representations may provide some objective measures of speech comprehension. 

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4. The intelligibility of consonants in American English infant-directed speech

   https://doi.org/10.1016/j.cogpsych.2025.101766  

   Swingley, Daniel (November 2025, Cognitive Psychology)

   To begin learning their language, infants must locate words in the speech signal. Some models of word discovery presuppose that the discovery process depends on identifying phonetic segments (phones) in speech. To test the plausibility of models arguing that infants can reliably categorize consonants in speech, adult native speakers were asked to identify the consonant in vowel-consonant-vowel sequences extracted from spontaneous English infant-directed speech. Listeners could consistently identify some instances of consonants (for example, correctly indicating that an /s/ was an /s/). But many tokens (about half) were not consistently identifiable. Performance was significantly worse for codas than onsets. Providing the full utterance context in low-pass-filtered form did not aid recognition, nor did familiarization with the talker. In a second task, listeners were barely above chance in guessing whether a consonant was a word onset or a word-final coda. Performance on infant-directed speech was not markedly better than performance on a comparison set of adult-directed speech consonants. Erroneous responses frequently had little systematic resemblance to the correct answer. The results suggest that it is not plausible that infants can parse most utterances exhaustively into strings of uttered speech sounds and feed those strings into a statistical clustering mechanism. 

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5. 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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