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Bunce, J., Soderstrom, M., Bergelson, E., Rosemberg, C., Stein, A., Alam, F., Migdalek, M. J., & Casillas, M. (2024). A cross-linguistic examination of young children’s everyday language experiences. Journal of Child Language. Advance online publication. doi:10.1017/S030500092400028X.
Abstract
We present an exploratory cross-linguistic analysis of the quantity of target-child-directed speech and adult-directed speech in North American English (US & Canadian), United Kingdom English, Argentinian Spanish, Tseltal (Tenejapa, Mayan), and Yélî Dnye (Rossel Island, Papuan), using annotations from 69 children aged 2–36 months. Using a novel methodological approach, our cross-linguistic and cross-cultural findings support prior work suggesting that target-child-directed speech quantities are stable across early development, while adult-directed speech decreases. A preponderance of speech from women was found to a similar degree across groups, with less target-child-directed speech from men and children in the North American samples than elsewhere. Consistently across groups, children also heard more adult-directed than target-child-directed speech. Finally, the numbers of talkers present in any given clip strongly impacted children’s moment-to-moment input quantities. These findings illustrate how the structure of home life impacts patterns of early language exposure across diverse developmental contexts.Additional information
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Casillas, M., Foushee, R., Méndez Girón, J., Polian, G., & Brown, P. (2024). Little evidence for a noun bias in Tseltal spontaneous speech. First Language, 44(6), 600-628. doi:10.1177/01427237231216571.
Abstract
This study examines whether children acquiring Tseltal (Mayan) demonstrate a noun bias – an overrepresentation of nouns in their early vocabularies. Nouns, specifically concrete and animate nouns, are argued to universally predominate in children’s early vocabularies because their referents are naturally available as bounded concepts to which linguistic labels can be mapped. This early advantage for noun learning has been documented using multiple methods and across a diverse collection of language populations. However, past evidence bearing on a noun bias in Tseltal learners has been mixed. Tseltal grammatical features and child–caregiver interactional patterns dampen the salience of nouns and heighten the salience of verbs, leading to the prediction of a diminished noun bias and perhaps even an early predominance of verbs. We here analyze the use of noun and verb stems in children’s spontaneous speech from egocentric daylong recordings of 29 Tseltal learners between 0;9 and 4;4. We find weak to no evidence for a noun bias using two separate analytical approaches on the same data; one analysis yields a preliminary suggestion of a flipped outcome (i.e. a verb bias). We discuss the implications of these findings for broader theories of learning bias in early lexical development. -
Lutzenberger, H., Casillas, M., Fikkert, P., Crasborn, O., & De Vos, C. (2024). More than looks: Exploring methods to test phonological discrimination in the sign language Kata Kolok. Language Learning and Development, 20(4), 297-323. doi:10.1080/15475441.2023.2277472.
Abstract
The lack of diversity in the language sciences has increasingly been criticized as it holds the potential for producing flawed theories. Research on (i) geographically diverse language communities and (ii) on sign languages is necessary to corroborate, sharpen, and extend existing theories. This study contributes a case study of adapting a well-established paradigm to study the acquisition of sign phonology in Kata Kolok, a sign language of rural Bali, Indonesia. We conducted an experiment modeled after the familiarization paradigm with child signers of Kata Kolok. Traditional analyses of looking time did not yield significant differences between signing and non-signing children. Yet, additional behavioral analyses (attention, eye contact, hand behavior) suggest that children who are signers and those who are non-signers, as well as those who are hearing and those who are deaf, interact differently with the task. This study suggests limitations of the paradigm due to the ecology of sign languages and the sociocultural characteristics of the sample, calling for a mixed-methods approach. Ultimately, this paper aims to elucidate the diversity of adaptations necessary for experimental design, procedure, and analysis, and to offer a critical reflection on the contribution of similar efforts and the diversification of the field.Additional information
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De Vos, C., Casillas, M., Uittenbogert, T., Crasborn, O., & Levinson, S. C. (2022). Predicting conversational turns: Signers’ and non-signers’ sensitivity to language-specific and globally accessible cues. Language, 98(1), 35-62. doi:10.1353/lan.2021.0085.
Abstract
Precision turn-taking may constitute a crucial part of the human endowment for communication. If so, it should be implemented similarly across language modalities, as in signed vs. spoken language. Here in the first experimental study of turn-end prediction in sign language, we find support for the idea that signed language, like spoken language, involves turn-type prediction and turn-end anticipation. In both cases, turns eliciting specific responses like questions accelerate anticipation. We also show remarkable cross-modality predictive capacity: non-signers anticipate sign turn-ends surprisingly well. Finally, we show that despite non-signers’ ability to intuitively predict signed turn-ends, early native signers do it much better by using their access to linguistic signals (here, question markers). As shown in prior work, question formation facilitates prediction, and age of sign language acquisition affects accuracy. The study thus sheds light on the kind of features that may facilitate turn-taking universally, and those that are language-specific.Additional information
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Bögels, S., Casillas, M., & Levinson, S. C. (2018). Planning versus comprehension in turn-taking: Fast responders show reduced anticipatory processing of the question. Neuropsychologia, 109, 295-310. doi:10.1016/j.neuropsychologia.2017.12.028.
Abstract
Rapid response latencies in conversation suggest that responders start planning before the ongoing turn is finished. Indeed, an earlier EEG study suggests that listeners start planning their responses to questions as soon as they can (Bögels, S., Magyari, L., & Levinson, S. C. (2015). Neural signatures of response planning occur midway through an incoming question in conversation. Scientific Reports, 5, 12881). The present study aimed to (1) replicate this early planning effect and (2) investigate whether such early response planning incurs a cost on participants’ concurrent comprehension of the ongoing turn. During the experiment participants answered questions from a confederate partner. To address aim (1), the questions were designed such that response planning could start either early or late in the turn. Our results largely replicate Bögels et al. (2015) showing a large positive ERP effect and an oscillatory alpha/beta reduction right after participants could have first started planning their verbal response, again suggesting an early start of response planning. To address aim (2), the confederate's questions also contained either an expected word or an unexpected one to elicit a differential N400 effect, either before or after the start of response planning. We hypothesized an attenuated N400 effect after response planning had started. In contrast, the N400 effects before and after planning did not differ. There was, however, a positive correlation between participants' response time and their N400 effect size after planning had started; quick responders showed a smaller N400 effect, suggesting reduced attention to comprehension and possibly reduced anticipatory processing. We conclude that early response planning can indeed impact comprehension processing.Additional information
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Cristia, A., Ganesh, S., Casillas, M., & Ganapathy, S. (2018). Talker diarization in the wild: The case of child-centered daylong audio-recordings. In Proceedings of Interspeech 2018 (pp. 2583-2587). doi:10.21437/Interspeech.2018-2078.
Abstract
Speaker diarization (answering 'who spoke when') is a widely researched subject within speech technology. Numerous experiments have been run on datasets built from broadcast news, meeting data, and call centers—the task sometimes appears close to being solved. Much less work has begun to tackle the hardest diarization task of all: spontaneous conversations in real-world settings. Such diarization would be particularly useful for studies of language acquisition, where researchers investigate the speech children produce and hear in their daily lives. In this paper, we study audio gathered with a recorder worn by small children as they went about their normal days. As a result, each child was exposed to different acoustic environments with a multitude of background noises and a varying number of adults and peers. The inconsistency of speech and noise within and across samples poses a challenging task for speaker diarization systems, which we tackled via retraining and data augmentation techniques. We further studied sources of structured variation across raw audio files, including the impact of speaker type distribution, proportion of speech from children, and child age on diarization performance. We discuss the extent to which these findings might generalize to other samples of speech in the wild. -
Räsänen, O., Seshadri, S., & Casillas, M. (2018). Comparison of syllabification algorithms and training strategies for robust word count estimation across different languages and recording conditions. In Proceedings of Interspeech 2018 (pp. 1200-1204). doi:10.21437/Interspeech.2018-1047.
Abstract
Word count estimation (WCE) from audio recordings has a number of applications, including quantifying the amount of speech that language-learning infants hear in their natural environments, as captured by daylong recordings made with devices worn by infants. To be applicable in a wide range of scenarios and also low-resource domains, WCE tools should be extremely robust against varying signal conditions and require minimal access to labeled training data in the target domain. For this purpose, earlier work has used automatic syllabification of speech, followed by a least-squares-mapping of syllables to word counts. This paper compares a number of previously proposed syllabifiers in the WCE task, including a supervised bi-directional long short-term memory (BLSTM) network that is trained on a language for which high quality syllable annotations are available (a “high resource language”), and reports how the alternative methods compare on different languages and signal conditions. We also explore additive noise and varying-channel data augmentation strategies for BLSTM training, and show how they improve performance in both matching and mismatching languages. Intriguingly, we also find that even though the BLSTM works on languages beyond its training data, the unsupervised algorithms can still outperform it in challenging signal conditions on novel languages.
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