Publications

Abstract

The mutual exclusivity (ME) assumption is proposed to facilitate early word learning by guiding infants to map novel words to novel referents. This study assessed the emergence and use of ME to both disambiguate and retain the meanings of novel words across development in 18‐month‐old monolingual and bilingual children (Experiment 1; N = 58), and in a sub‐group of these children again at 24 months of age (Experiment 2: N = 32). Both monolinguals and bilinguals employed ME to select the referent of a novel label to a similar extent at 18 and 24 months. At 18 months, there were also no differences in novel word retention between the two language‐background groups. However, at 24 months, only monolinguals showed the ability to retain these label–object mappings. These findings indicate that the development of the ME assumption as a reliable word‐learning strategy is shaped by children's individual language exposure and experience with language use.

Abstract

We report a large‐scale electrophysiological study of infant speech segmentation, in which over 100 English‐acquiring 9‐month‐olds were exposed to unfamiliar bisyllabic words embedded in sentences (e.g., He saw a wild eagle up there), after which their brain responses to either the just‐familiarized word (eagle) or a control word (coral) were recorded. When initial exposure occurs in continuous speech, as here, past studies have reported that even somewhat older infants do not reliably recognize target words, but that successful segmentation varies across children. Here, we both confirm and further uncover the nature of this variation. The segmentation response systematically varied across individuals and was related to their vocabulary development. About one‐third of the group showed a left‐frontally located relative negativity in response to familiar versus control targets, which has previously been described as a mature response. Another third showed a similarly located positive‐going reaction (a previously described immature response), and the remaining third formed an intermediate grouping that was primarily characterized by an initial response delay. A fine‐grained group‐level analysis suggested that a developmental shift to a lexical mode of processing occurs toward the end of the first year, with variation across individual infants in the exact timing of this shift.

Abstract

Humans differ in innumerable ways, with considerable variation observable at every level of description, from the molecular to the social. Traditionally, linguistic and psycholinguistic theory has downplayed the possibility of meaningful differences in language across individuals. However, it is becoming increasingly evident that there is
significant variation among speakers at any age as well as across the lifespan. In this paper, we review recent research in psycholinguistics, and argue that a focus on individual differences provides a crucial source of evidence that bears strongly upon core issues in theories of the acquisition and processing of language; specifically, the role of experience in language acquisition, processing, and attainment, and the architecture of the language faculty.

Abstract

A developmental relationship between symbolic play and language has been long proposed, going as far back as the writings of Piaget and Vygotsky. In the current paper we build on recent qualitative reviews of the literature by reporting the first quantitative analysis of the relationship. We conducted a three-level meta-analysis of past studies that have investigated the relationship between symbolic play and language acquisition. Thirty-five studies (N = 6848) met the criteria for inclusion. Overall, we observed a significant small-to-medium association between the two domains (r = .35). Several moderating variables were included in the analyses, including: (i) study design (longitudinal, concurrent), (ii) the manner in which language was measured (comprehension, production), and (iii) the age at which this relationship is measured. The effect was weakly moderated by these three variables, but overall the association was robust, suggesting that symbolic play and language are closely related in development.

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.

Abstract

Before infants become mature speakers of their native language, they must acquire a robust word-recognition system which allows them to strike the balance between allowing some variation (mood, voice, accent) and recognizing variability that potentially changes meaning (e.g. cat vs hat). The current meta-analysis quantifies how the latter, termed mispronunciation sensitivity, changes over infants’ first three years, testing competing predictions of mainstream language acquisition theories. Our results show that infants were sensitive to mispronunciations, but accepted them as labels for target objects. Interestingly, and in contrast to predictions of mainstream theories, mispronunciation sensitivity was not modulated by infant age, suggesting that a sufficiently flexible understanding of native language phonology is in place at a young age.

Abstract

We used eye-tracking to investigate if and when children show an incremental bias to assume that the first noun phrase in a sentence is the agent (first-NP-as-agent bias) while processing the meaning of English active and passive transitive sentences. We also investigated whether children can override this bias to successfully distinguish active from passive sentences, after processing the remainder of the sentence frame. For this second question we used eye-tracking (Study 1) and forced-choice pointing (Study 2). For both studies, we used a paradigm in which participants simultaneously saw two novel actions with reversed agent-patient relations while listening to active and passive sentences. We compared English-speaking 25-month-olds and 41-month-olds in between-subjects sentence structure conditions (Active Transitive Condition vs. Passive Condition). A permutation analysis found that both age groups showed a bias to incrementally map the first noun in a sentence onto an agent role. Regarding the second question, 25-month-olds showed some evidence of distinguishing the two structures in the eye-tracking study. However, the 25-month-olds did not distinguish active from passive sentences in the forced choice pointing task. In contrast, the 41-month-old children did reanalyse their initial first-NP-as-agent bias to the extent that they clearly distinguished between active and passive sentences both in the eye-tracking data and in the pointing task. The results are discussed in relation to the development of syntactic (re)parsing.

Abstract

Interoperable annotation formats are fundamental to the utility, expansion, and sustainability of collective data repositories.In language development research, shared annotation schemes have been critical to facilitating the transition from raw acoustic data to searchable, structured corpora. Current schemes typically require comprehensive and manual annotation of utterance boundaries and orthographic speech content, with an additional, optional range of tags of interest. These schemes have been enormously successful for datasets on the scale of dozens of recording hours but are untenable for long-format recording corpora, which routinely contain hundreds to thousands of audio hours. Long-format corpora would benefit greatly from (semi-)automated analyses, both on the earliest steps of annotation—voice activity detection, utterance segmentation, and speaker diarization—as well as later steps—e.g., classification-based codes such as child-vs-adult-directed speech, and speech recognition to produce phonetic/orthographic representations. We present an annotation workflow specifically designed for long-format corpora which can be tailored by individual researchers and which interfaces with the current dominant scheme for short-format recordings. The workflow allows semi-automated annotation and analyses at higher linguistic levels. We give one example of how the workflow has been successfully implemented in a large cross-database project.

Abstract

Child-directed speech is argued to facilitate language development, and is found cross-linguistically and cross-culturally to varying degrees. However, previous research has generally focused on short samples of child-caregiver interaction, often in the lab or with experimenters present. We test the generalizability of this phenomenon with an initial descriptive analysis of the speech heard by young children in a large, unique collection of naturalistic, daylong home recordings. Trained annotators coded automatically-detected adult speech 'utterances' from 61 homes across 4 North American cities, gathered from children (age 2-24 months) wearing audio recorders during a typical day. Coders marked the speaker gender (male/female) and intended addressee (child/adult), yielding 10,886 addressee and gender tags from 2,523 minutes of audio (cf. HB-CHAAC Interspeech ComParE challenge; Schuller et al., in press). Automated speaker-diarization (LENA) incorrectly gender-tagged 30% of male adult utterances, compared to manually-coded consensus. Furthermore, we find effects of SES and gender on child-directed and overall speech, increasing child-directed speech with child age, and interactions of speaker gender, child gender, and child age: female caretakers increased their child-directed speech more with age than male caretakers did, but only for male infants. Implications for language acquisition and existing classification algorithms are discussed.

Abstract

Children who hear large amounts of diverse speech learn language more quickly than children who do not. However, high correlations between the amount and the diversity of the input in speech samples makes it difficult to isolate the influence of each. We overcame this problem by controlling the input to a computational model so that amount of exposure to linguistic input (quantity) and the quality of that input (lexical diversity) were independently manipulated. Sublexical, lexical, and multi-word knowledge were charted across development (Study 1), showing that while input quantity may be important early in learning, lexical diversity is ultimately more crucial, a prediction confirmed against children’s data (Study 2). The model trained on a lexically diverse input also performed better on nonword repetition and sentence recall tests (Study 3) and was quicker to learn new words over time (Study 4). A language input that is rich in lexical diversity outperforms equivalent richness in quantity for learned sublexical and lexical knowledge, for well-established language tests, and for acquiring words that have never been encountered before.

Abstract

Historically, first language acquisition research was a painstaking process of observation, requiring the laborious hand coding of children's linguistic productions, followed by the generation of abstract theoretical proposals for how the developmental process unfolds. Recently, the ability to collect large-scale corpora of children's language exposure has revolutionized the field. New techniques enable more precise measurements of children's actual language input, and these corpora constrain computational and cognitive theories of language development, which can then generate predictions about learning behavior. We describe several instances where corpus, computational, and experimental work have been productively combined to uncover the first language acquisition process and the richness of multimodal properties of the environment, highlighting how these methods can be extended to address related issues in second language research. Finally, we outline some of the difficulties that can be encountered when applying multimethod approaches and show how these difficulties can be obviated

Abstract

In developmental psycholinguistics, we have, for many years,
been generating and testing theories that propose both descriptions of
adult representations and explanations of how those representations
develop. We have learnt that restricting ourselves to any one
methodology yields only incomplete data about the nature of linguistic
representations. We argue that we need a multi-method approach to the
study of representation.