Relationships Between Vocabulary Size, Working Memory, and Phonological Awareness in Spanish-Speaking English Language Learners

Purpose: The goals of this study were to evaluate the impact of short-term phonological awareness (PA) instruction presented in children\u27s first language (L1; Spanish) on gains in their L1 and second language (L2; English) and to determine whether relationships exist between vocabulary size, verbal working memory, and PA in Spanish-speaking English language learners (ELLs). Method: Participants included 25 kindergartners who received PA instruction and 10 controls. A 2-way within-subjects repeated measures multivariate analysis of variance (MANOVA) was conducted to evaluate gains. Relationships between PA gains, Spanish and English vocabulary, and memory, as measured using nonword repetition and experimental working memory tasks, were analyzed using correlation and regression analyses. Results: Results indicated significant and equivalent gains in both languages of children in the experimental group and no gains in the control group. Spanish vocabulary size was significantly related to PA gains in both languages and was more strongly related to English gains than was English vocabulary size. The memory tasks predicted gains in each language in distinct ways. Conclusion: Results support the conclusion that PA instruction and strong vocabulary skills in an individual\u27s L1 benefit PA development in both the L1 and L2. Results also indicate that dynamic relationships exist between vocabulary size, storage and processing components of working memory, and PA development in both languages of ELLs

Speech articulation in children with Williams syndrome or 7q11.23 duplication syndrome.

Williams syndrome (WS) and 7q11.23 duplication syndrome (Dup7) are associated with communication disorders (Huffman et al., 2013). However, articulatory accuracy has not been systematically examined in these populations. The dissertation involved two studies. Using standardized citation assessment, Study 1 addressed articulatory accuracy with regard to age norms and differences between groups. Results indicated that for both groups, (a) consonant accuracy was significantly below expectations, (b) older children pronounced consonants with significantly better accuracy than younger children, (c) children with IQs at or above 70 earned significantly higher articulation standard scores, and (d) for particular groups of consonant sounds, arranged as a function of features of articulation, significant differences were found across consonant groups for (c.1) expected period of acquisition in development, (c.2) articulatory place of production, (c.3) articulatory manner of production, and (c.4) movement transition across consonants within clusters. Study 2 addressed variance relations among speech articulatory accuracy, phonological processing, and particular cognitive and linguistic measures. Articulatory accuracy was shown moderately, to strongly, related to each study variable. For the children with WS, articulatory accuracy contributed unique variance to phonological processing beyond that contributed by verbal short-term memory, spatial ability, and the combined factor of lexical understanding and use. Overall, the results showed children in both groups were significant delayed in consonantal development. Patterns of articulatory accuracy did not differ greatly from those of younger, typically developing children. Furthermore, the findings demonstrated positive relations among articulatory accuracy, phonological processing, intellectual abilities, and vocabulary abilities for children with these syndromes

Le langage et son acquisition : bases biologiques et psychologiques

Emmanuel Dupoux, directeur d’étudesChristophe Pallier, chargé de recherche au CNRS Ateliers pratiques et théoriques en sciences cognitives Cet enseignement a pour but d’aborder de façon concrète quelques-unes des concepts clefs dans chacune des disciplines fondatrices des sciences cognitives. Les sessions sont organisées sur un mode collaboratif et interactif ; les étudiants sont distribués dans des petits groupes interdisciplinaires et travaillent sur des textes, des données ou des exercices..

Exploring phonological relationships between babbling and early word productions

In this research, I set out to uncover relationships between the phonological composition of the babbled utterances and early word productions. I track segmental development in onset position across both babbled utterances and early word productions of two English-learning children (Cameron and Georgia) from the English-Davis corpus, available through CHILDES/Phonbank ( http://childes.talkbank.org/phon ). Both children display a very strong tendency to produce sounds in babbled utterances before attempting them in meaningful words. Also, these children show very little variation away from English phonemes in their babbled utterances, which suggests that these children have a good level of awareness of the native phonological system from a young age. However, a close examination of the treatment of [l] in Cameron's babbles and early word productions suggests that at least certain segments receive different treatment at different stages of the child's phonological development. I also perform a formal analysis of the productions of both children using descriptive features and Feature Co-occurrence Constraints (FCCs). Both children's phonological development can be can be captured using this model. However, many of the differences in their developmental paths remain unaccounted for given this type of analysis. Addressing this issue, I conduct an analysis of both children's substitution patterns to determine what factors (e.g. perceptual or articulatory) influenced their productions of unacquired segments. As we will see, many of the substitution patterns displayed by both children appear to have strong articulatory influences. The knowledge gained from each of these analyses highlights the benefits of using a multi-faceted approach to phonological acquisition

Unsupervised learning of Arabic non-concatenative morphology

Unsupervised approaches to learning the morphology of a language play an important role in computer processing of language from a practical and theoretical perspective, due their minimal reliance on manually produced linguistic resources and human annotation. Such approaches have been widely researched for the problem of concatenative affixation, but less attention has been paid to the intercalated (non-concatenative) morphology exhibited by Arabic and other Semitic languages. The aim of this research is to learn the root and pattern morphology of Arabic, with accuracy comparable to manually built morphological analysis systems. The approach is kept free from human supervision or manual parameter settings, assuming only that roots and patterns intertwine to form a word. Promising results were obtained by applying a technique adapted from previous work in concatenative morphology learning, which uses machine learning to determine relatedness between words. The output, with probabilistic relatedness values between words, was then used to rank all possible roots and patterns to form a lexicon. Analysis using trilateral roots resulted in correct root identification accuracy of approximately 86% for inflected words. Although the machine learning-based approach is effective, it is conceptually complex. So an alternative, simpler and computationally efficient approach was then devised to obtain morpheme scores based on comparative counts of roots and patterns. In this approach, root and pattern scores are defined in terms of each other in a mutually recursive relationship, converging to an optimized morpheme ranking. This technique gives slightly better accuracy while being conceptually simpler and more efficient. The approach, after further enhancements, was evaluated on a version of the Quranic Arabic Corpus, attaining a final accuracy of approximately 93%. A comparative evaluation shows this to be superior to two existing, well used manually built Arabic stemmers, thus demonstrating the practical feasibility of unsupervised learning of non-concatenative morphology

Modeling Word Forms Using Latent Underlying Morphs and Phonology

The observed pronunciations or spellings of words are often explained as arising from the “underlying forms” of their mor- phemes. These forms are latent strings that linguists try to reconstruct by hand. We propose to reconstruct them automatically at scale, enabling generalization to new words. Given some surface word types of a concatenative language along with the abstract morpheme sequences that they ex- press, we show how to recover consistent underlying forms for these morphemes, together with the (stochastic) phonology that maps each concatenation of underly- ing forms to a surface form. Our technique involves loopy belief propagation in a nat- ural directed graphical model whose vari- ables are unknown strings and whose con- ditional distributions are encoded as finite- state machines with trainable weights. We define training and evaluation paradigms for the task of surface word prediction, and report results on subsets of 7 languages