Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic

Language modeling for an inflected language such as Arabic poses new challenges for speech recognition and machine translation due to its rich morphology. Rich morphology results in large increases in out-of-vocabulary (OOV) rate and poor language model parameter estimation in the absence of large quantities of data. In this study, we present a joint morphological-lexical language model (JMLLM) that takes advantage of Arabic morphology. JMLLM combines morphological segments with the underlying lexical items and additional available information sources with regards to morphological segments and lexical items in a single joint model. Joint representation and modeling of morphological and lexical items reduces the OOV rate and provides smooth probability estimates while keeping the predictive power of whole words. Speech recognition and machine translation experiments in dialectal-Arabic show improvements over word and morpheme based trigram language models. We also show that as the tightness of integration between different information sources increases, both speech recognition and machine translation performances improve

The Effect of Speech Elicitation Method on Second Language Phonemic Accuracy

The present study, a One-Group Posttest-Only Repeated-Measures Design, examined the effect of speech elicitation method on second language (L2) phonemic accuracy of high functional load initial phonemes found in frequently occurring nouns in American English. This effect was further analyzed by including the variable of first language (L1) to determine if L1 moderated any effects found. The data consisted of audio recordings of 61 adult English learners (ELs) enrolled in English for Academic Purposes (EAP) courses at a large, public, post-secondary institution in the United States. Phonemic accuracy was judged by two independent raters as either approximating a standard American English (SAE) pronunciation of the intended phoneme or not, thus a dichotomous scale, and scores were assigned to each participant in terms of the three speech elicitation methods of word reading, word repetition, and picture naming. Results from a repeated measures ANOVA test revealed a statistically significant difference in phonemic accuracy (F(1.47, 87.93) = 25.94, p = .000) based on speech elicitation method, while the two-factor mixed design ANOVA test indicated no statistically significant differences for the moderator variable of native language. However, post-hoc analyses revealed that mean scores of picture naming tasks differed significantly from the other two elicitation methods of word reading and word repetition. Moreover, the results of this study should heighten attention to the role that various speech elicitation methods, or input modalities, might play on L2 productive accuracy. Implications for practical application suggest that caution should be used when utilizing pictures to elicit specific vocabulary words–even high-frequency words–as they might result in erroneous productions or no utterance at all. These methods could inform pronunciation instructors about best teaching practices when pronunciation accuracy is the objective. Finally, the impact of L1 on L2 pronunciation accuracy might not be as important as once thought

The relationship between arabic language proficiency, english language proficiency, and science academic achievement of 11th grade arabic speaking english language learners

Limited schooling in the first language (L1) has allowed English Language Learners (ELLs) to face obstacles in their second language (L2) and science courses. Therefore, this study examines these variables in the following two hypotheses: (1) there is a significant relationship between Arabic language proficiency and English language proficiency and (2) there is a significant relationship between Arabic language proficiency and science academic achievement. A causal-comparative design was used to examine these hypotheses. The investigator selected sixty 11th grade Arabic-speaking students based on a nonrandom sampling method from one high school in the Metropolitan Schools (pseudonym) in Southeast Michigan. The measures used to collect data include: (1) Versant Arabic Test (VAT), (2) English Language Proficiency Assessment (ELPA), and (3) Science component of the Michigan Merit Examination (MME). Descriptive analysis classified the sixty students by country of origin, age, gender and ESL level. Inferential statistics that were used to investigate the research hypotheses included correlational analysis and multivariate regression analysis. The results of correlational and multivariate regressional analyses showed a significant relationship between Arabic language proficiency and English language proficiency. Thus, the first hypothesis was supported. However, no significant relationship was found between Arabic language proficiency and science academic achievement, when conducting correlational and multiple regression analysis. Thus, the second hypothesis was not supported. Discussions are provided as to why the first hypothesis was supported and as to why the second hypothesis was not supported. Also, educational implications as well as directions for future research are provided

Articulation rate as a metric in spoken language assessment

Copyright © 2019 ISCA Automated evaluation of non-native pronunciation provides a consistent and more cost-efficient alternative to human evaluation. To that end, there is considerable interest in deriving metrics that are based on the cues human listeners use to judge pronunciation. Previous research reported the use of phonetic features such as vowel characteristics in automated spoken language evaluation. The present study extends this line of work on the significance of phonetic features in automated evaluation of L2 speech (both assessment and feedback). Predictive modelling techniques examined the relationship between various articulation rate metrics one the one hand, and the proficiency and L1 background of non-native English speakers on the other. It was found that the optimal predictive model was one in which the phonetic details of phoneme articulation were factored in the analysis of articulation rate. Model performance varied also according to the L1 background of speakers. The implications for assessment and feedback are discussed.Leverhulme ECF Fellowship; ALTA projec

Orthographic Learning in Arabic-Speaking Primary School Students

The aim of this study was to examine how Arabic-speaking children construct orthographic representations and to identify cognitive/linguistic abilities that may facilitate novel word learning. The research involved first examining factors associated with single word reading and spelling accuracy in Arabic-speaking monolingual children and Arabic-English bilingual children, in order to separate universal from script-dependent predictors. Because Arabic is diglossic (i.e., two varieties of the language, one spoken, and one for literary purposes), it was considered important to include print exposure as a measure in investigating factors associated with single word reading and spelling. Thus, Study 1 involved the development of Title Recognition Tests (TRT) in Arabic and in English. Participants were children from grades three to five; 86 students participated in the development of the lists in Study 1a, and 76 in the development of the revised lists in Study 1b. Both lists were reliable and were used in the subsequent studies. Study 2 involved examining predictors of single word reading and spelling (receptive vocabulary, phonological processing, RAN, TRT, and orthographic matching) in 86 third- to fifth-grade bilingual children and 116 third-grade monolingual children. For the bilinguals, PA emerged as the strongest predictor of reading and spelling in Arabic. In English, verbal STM and orthographic matching were predictors for the younger bilinguals. PA was the strongest predictor of reading and spelling for the monolinguals. In Study 3, novel word learning in Arabic was examined using a paired-associate learning task, orthography present or absent and varying visual complexity (ligature and diacritics). The 116 monolingual children from Study 2 participated. Child-related predictors of novel word learning were examined. Results revealed that presence of orthography facilitated learning. There was evidence that consonant diacritics are a source of difficulty, but diglossic phonemes may also be responsible for reading difficulties documented in Arabic

The Effect of Shadowing in Learning L2 Segments: A Perspective from Phonetic Convergence

This study aimed to investigate the role that phonetic convergence plays in the acquisition of L2 segments. In particular, it examined whether phonetic convergence towards native speakers could help Arabic-speaking second-language (L2) learners of English improve their pronunciation of four problematic English segments (/p, v, ɛ, oʊ/). To do so, the study went through several phases of experimental studies. Phonetic convergence was first explored in the productions of Arabic L2 learners towards five different English native model talkers in non-interactive setting. Five XAB perceptual similarity judgments and acoustic measurements of VOT, vowel duration, F0, and F1*F2 were used to evaluate phonetic convergence.Based mainly on perceptual measures of phonetic convergence, learners were divided evenly between two groups. C-group (convergence group) received phonetic production training from the model talkers to whom they showed the highest degree of phonetic convergence, while D-group (divergence group) received training from the model talkers they showed divergence from or the least convergence to. Training lasted three consecutive days with target segments (i.e., /p, v, ɛ, oʊ/) presented in nonsense words. They were trained using the shadowing technique that used low-variability training paradigm in which each learner received training from one native model talker. Native-speaker judgments on segmental intelligibility indicated both groups showed significant improvement on the post-test; however, no significant differences were found between groups in terms of the overall magnitude of this change. Perceived convergence in learners’ speech failed to explain the improvement. However, some patterns of acoustic convergence towards their trainers, regardless of group, predicted the overall segmental intelligibility gains. The findings suggested that the more trainees converged their vowel duration and formants to their trainers, the more their performance improved. At featural level, the study examined the relationship between the preexisting phonetic distance between the Arabic L2 learners of English and model talkers before the exposure and the degree of convergence. Results indicated that there was a direct relationship between how far Arabic L2 learners were from the native model talkers and the degree of convergence in all measured acoustic features. That is, the greater the baseline distance, the greater the degree of phonetic convergence was. However, such a relationship might be due to the metric used to assess phonetic convergence. The relationship between phonetic convergence measured by difference in distance (DID) and the absolute baseline distance is always biased due to the way they are calculated (Cohen Priva & Sanker, 2019; MacLeod, 2021). This study found shadowing to be an effective technique to promote segmental intelligibility among Arabic-speakers learning English as an L2. However, this effectiveness might be increased by trainees converging more to their trainers in vowel duration and vowel spectra or being similar to their trainers in this regard from the beginning

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI