Phonotactic probability and phonotactic constraints :processing and lexical segmentation by Arabic learners of English as a foreign language

PhD ThesisA fundamental skill in listening comprehension is the ability to recognize words. The ability to accurately locate word boundaries(i . e. to lexically segment) is an important contributor to this skill. Research has shown that English native speakers use various cues in the signal in lexical segmentation. One such cue is phonotactic constraints; more specifically, the presence of illegal English consonant sequences such as AV and MY signals word boundaries. It has also been shown that phonotactic probability (i. e. the frequency of segments and sequences of segments in words) affects native speakers' processing of English. However, the role that phonotactic probability and phonotactic constraints play in the EFL classroom has hardly been studied, while much attention has been devoted to teaching listening comprehension in EFL. This thesis reports on an intervention study which investigated the effect of teaching English phonotactics upon Arabic speakers' lexical segmentation of running speech in English. The study involved a native English group (N= 12), a non-native speaking control group (N= 20); and a non-native speaking experimental group (N=20). Each of the groups took three tests, namely Non-word Rating, Lexical Decision and Word Spotting. These tests probed how sensitive the subjects were to English phonotactic probability and to the presence of illegal sequences of phonemes in English and investigated whether they used these sequences in the lexical segmentation of English. The non-native groups were post-tested with the -same tasks after only the experimental group had been given a treatment which consisted of explicit teaching of relevant English phonotactic constraints and related activities for 8 weeks. The gains made by the experimental group are discussed, with implications for teaching both pronunciation and listening comprehension in an EFL setting.Qassim University, Saudi Arabia

The cross-linguistic performance of word segmentation models over time.

We select three word segmentation models with psycholinguistic foundations - transitional probabilities, the diphone-based segmenter, and PUDDLE - which track phoneme co-occurrence and positional frequencies in input strings, and in the case of PUDDLE build lexical and diphone inventories. The models are evaluated on caregiver utterances in 132 CHILDES corpora representing 28 languages and 11.9 m words. PUDDLE shows the best performance overall, albeit with wide cross-linguistic variation. We explore the reasons for this variation, fitting regression models to performance scores with linguistic properties which capture lexico-phonological characteristics of the input: word length, utterance length, diversity in the lexicon, the frequency of one-word utterances, the regularity of phoneme patterns at word boundaries, and the distribution of diphones in each language. These properties together explain four-tenths of the observed variation in segmentation performance, a strong outcome and a solid foundation for studying further variables which make the segmentation task difficult

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Rhythmic unit extraction and modelling for automatic language identification

International audienceThis paper deals with an approach to Automatic Language Identification based on rhythmic modelling. Beside phonetics and phonotactics, rhythm is actually one of the most promising features to be considered for language identification, even if its extraction and modelling are not a straightforward issue. Actually, one of the main problems to address is what to model. In this paper, an algorithm of rhythm extraction is described: using a vowel detection algorithm, rhythmic units related to syllables are segmented. Several parameters are extracted (consonantal and vowel duration, cluster complexity) and modelled with a Gaussian Mixture. Experiments are performed on read speech for 7 languages (English, French, German, Italian, Japanese, Mandarin and Spanish) and results reach up to 86 ± 6% of correct discrimination between stress-timed mora-timed and syllable-timed classes of languages, and to 67 ± 8% percent of correct language identification on average for the 7 languages with utterances of 21 seconds. These results are commented and compared with those obtained with a standard acoustic Gaussian mixture modelling approach (88 ± 5% of correct identification for the 7-languages identification task)

The effectiveness of a computer-supported intervention targeting phonological recoding and orthographic processing for children with word reading impairment

This research designed, developed, trialled, and evaluated a reading intervention targeting phonological recoding and orthographic processing for children with persistent reading impairment. Eight otherwise typically developing Year 2 participants with reading delay despite previous intervention, were randomly assigned to two groups in a single subject multiple-treatment cross-over design study. The results of group and individual analyses indicated that all participants made significant gains on measures of nonword reading with trends for gains in word reading

Implicit foreign language learning after minimal exposure across the lifespan

Langues étrangères