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Journal articles on the topic 'Language samples'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Barokova, Mihaela, and Helen Tager-Flusberg. "Commentary: Measuring Language Change Through Natural Language Samples." Journal of Autism and Developmental Disorders 50, no. 7 (June 5, 2018): 2287–306. http://dx.doi.org/10.1007/s10803-018-3628-4.

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Long, Steven H. "‘Computerized profiling’ of clinical language samples." Clinical Linguistics & Phonetics 1, no. 1 (January 1987): 97–105. http://dx.doi.org/10.1080/02699208708985005.

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Murthy, Kavi Narayana, and G. Bharadwaja Kumar. "Language identification from small text samples*." Journal of Quantitative Linguistics 13, no. 1 (January 2006): 57–80. http://dx.doi.org/10.1080/09296170500500694.

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Costanza-Smith, Amy. "The Clinical Utility of Language Samples." Perspectives on Language Learning and Education 17, no. 1 (March 2010): 9–15. http://dx.doi.org/10.1044/lle17.1.9.

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Abstract Speech-language pathologists typically use standardized assessments to diagnose language disorders. Although standardized tests are important in diagnosing school-age language disorders, the use of language sample analysis should not be ignored. This article summarizes the benefits of language sample analysis and introduces considerations for collecting and analyzing language samples.

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Eisenberg, Sarita L., and Ling-Yu Guo. "Sample Size for Measuring Grammaticality in Preschool Children From Picture-Elicited Language Samples." Language, Speech, and Hearing Services in Schools 46, no. 2 (April 2015): 81–93. http://dx.doi.org/10.1044/2015_lshss-14-0049.

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Purpose The purpose of this study was to investigate whether a shorter language sample elicited with fewer pictures (i.e., 7) would yield a percent grammatical utterances (PGU) score similar to that computed from a longer language sample elicited with 15 pictures for 3-year-old children. Method Language samples were elicited by asking forty 3-year-old children with varying language skills to talk about pictures in response to prompts. PGU scores were computed for each of two 7-picture sets and for the full set of 15 pictures. Results PGU scores for the two 7-picture sets did not differ significantly from, and were highly correlated with, PGU scores for the full set and with each other. Agreement for making pass–fail decisions between each 7-picture set and the full set and between the two 7-picture sets ranged from 80% to 100%. Conclusion The current study suggests that the PGU measure is robust enough that it can be computed on the basis of 7, at least in 3-year-old children whose language samples were elicited using similar procedures.

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Channell, Ron W., and Bonnie W. Johnson. "Automated Grammatical Tagging of Child Language Samples." Journal of Speech, Language, and Hearing Research 42, no. 3 (June 1999): 727–34. http://dx.doi.org/10.1044/jslhr.4203.727.

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Recent studies of the automated grammatical categorization ("tagging") of words using probabilistic methods have reported substantial levels of accuracy—over 95% agreement with manual tagging for words from a variety of texts. However, the texts with which this method has been tested were written by adults and edited by publishers. The present study examined the accuracy with which such methods could tag transcribed conversational language samples from 30 normally developing children. On a word-by-word basis, automated accuracy levels ranged from 92.9% to 97.4%, averaging 95.1%. Accuracy at correctly tagging whole utterances was lower, ranging from 60.5% to 90.3%, with an average of 77.7%. Probabilistic methods of coding language samples hold potential as a viable tool for child language research. Further study and improvement of automated grammatical tagging is warranted and necessary before widespread use can be made of this technology.

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Nelson, Nickola Wolf, and Adelia M. Van Meter. "Measuring Written Language Ability in Narrative Samples." Reading & Writing Quarterly 23, no. 3 (April 25, 2007): 287–309. http://dx.doi.org/10.1080/10573560701277807.

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Rijkhoff, Jan, Dik Bakker, Kees Hengeveld, and Peter Kahrel. "A Method of Language Sampling." Studies in Language 17, no. 1 (January 1, 1993): 169–203. http://dx.doi.org/10.1075/sl.17.1.07rij.

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In recent years more attention is being paid to the quality of language samples in typological work. Without an adequate sampling strategy, samples may suffer from various kinds of bias. In this article we propose a sampling method in which the genetic criterion is taken as the most important: samples created with this method will reflect optimally the diversity of the languages of the world. On the basis of the internal structure of each genetic language tree a measure is computed that reflects the linguistic diversity in the language families represented by these trees. This measure is used to determine how many languages from each phylum should be selected, given any required sample size.

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Wren, Carol T. "Collecting Language Samples from Children with Syntax Problems." Language, Speech, and Hearing Services in Schools 16, no. 2 (April 1985): 83–102. http://dx.doi.org/10.1044/0161-1461.1602.83.

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Although it is important that children produce enough spontaneous language to be analyzed, length is not the only criterion to be considered when collecting language samples. This article suggests that comparability, representativeness, and typicality must also be considered when selecting language elicitation tasks: A framework is suggested as a basis for selecting or devising tasks, and one successful battery is described which has been field tested on language-disordered children with syntax problems. These tasks elicit a wide variety of language from the children as well as stimulate them to produce a large, representative corpus of utterances.

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Tommerdahl, Jodi, and Cynthia D. Kilpatrick. "The reliability of morphological analyses in language samples." Language Testing 31, no. 1 (July 2, 2013): 3–18. http://dx.doi.org/10.1177/0265532213485570.

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LOEWEN, SHAWN, and BRONSON HUI. "Small Samples in Instructed Second Language Acquisition Research." Modern Language Journal 105, no. 1 (March 2021): 187–93. http://dx.doi.org/10.1111/modl.12700.

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Manning, Brittany L., Alexandra Harpole, Emily M. Harriott, Kamila Postolowicz, and Elizabeth S. Norton. "Taking Language Samples Home: Feasibility, Reliability, and Validity of Child Language Samples Conducted Remotely With Video Chat Versus In-Person." Journal of Speech, Language, and Hearing Research 63, no. 12 (December 14, 2020): 3982–90. http://dx.doi.org/10.1044/2020_jslhr-20-00202.

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Purpose There has been increased interest in using telepractice for involving more diverse children in research and clinical services, as well as when in-person assessment is challenging, such as during COVID-19. Little is known, however, about the feasibility, reliability, and validity of language samples when conducted via telepractice. Method Child language samples from parent–child play were recorded either in person in the laboratory or via video chat at home, using parents' preferred commercially available software on their own device. Samples were transcribed and analyzed using Systematic Analysis of Language Transcripts software. Analyses compared measures between-subjects for 46 dyads who completed video chat language samples versus 16 who completed in-person samples; within-subjects analyses were conducted for a subset of 13 dyads who completed both types. Groups did not differ significantly on child age, sex, or socioeconomic status. Results The number of usable samples and percent of utterances with intelligible audio signal did not differ significantly for in-person versus video chat language samples. Child speech and language characteristics (including mean length of utterance, type–token ratio, number of different words, grammatical errors/omissions, and child speech intelligibility) did not differ significantly between in-person and video chat methods. This was the case for between-group analyses and within-child comparisons. Furthermore, transcription reliability (conducted on a subset of samples) was high and did not differ between in-person and video chat methods. Conclusions This study demonstrates that child language samples collected via video chat are largely comparable to in-person samples in terms of key speech and language measures. Best practices for maximizing data quality for using video chat language samples are provided.

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Miller, Jon F., Karen Andriacchi, and Ann Nockerts. "Using Language Sample Analysis to Assess Spoken Language Production in Adolescents." Language, Speech, and Hearing Services in Schools 47, no. 2 (April 2016): 99–112. http://dx.doi.org/10.1044/2015_lshss-15-0051.

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Purpose This tutorial discusses the importance of language sample analysis and how Systematic Analysis of Language Transcripts (SALT) software can be used to simplify the process and effectively assess the spoken language production of adolescents. Method Over the past 30 years, thousands of language samples have been collected from typical speakers, aged 3–18 years, in conversational and narrative contexts. These samples have been formatted as reference databases included with SALT. Using the SALT software, individual samples are compared with age- and grade-matched samples selected from these databases. Results Two case studies illustrate that comparison with database samples of typical adolescents, matched by grade and elicitation context, highlights language measures that are higher or lower than the database mean values. Differences in values are measured in standard deviations. Conclusion Language sample analysis remains a powerful method of documenting language use in everyday speaking situations. A sample of talking reveals an individual's ability to meet specific speaking demands. These demands vary across contexts, and speakers can have difficulty in any one or all of these communication tasks. Language use for spoken communication is a foundation for literacy attainment and contributes to success in navigating relationships for school, work, and community participation.

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Gutiérrez-Clellen, Vera F., M. Adelaida Restrepo, Lisa Bedore, Elizabeth Peña, and Raquel Anderson. "Language Sample Analysis in Spanish-Speaking Children." Language, Speech, and Hearing Services in Schools 31, no. 1 (January 2000): 88–98. http://dx.doi.org/10.1044/0161-1461.3101.88.

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The purposes of this article are (a) to discuss issues related to the selection and development of language assessment procedures for children who speak Spanish and English based on spontaneous language samples and (b) to show how available procedures can be applied to research and clinical aims with these children. Sociolinguistic influences in the language performance of Spanish-speaking children, including patterns of language shift, differences in the amount of exposure to each of a bilingual's languages, and contextual effects of different language-learning environments, are discussed. Methodological issues and effects of codeswitching and dialect are examined concerning use of the Developmental Assessment of Spanish Grammar (DASG), mean length of response in words (MLR-w), mean length of terminable unit (MLTU), and mean length of utterance in morphemes (MLU-m). Measures of Spanish grammar with diagnostic potential are proposed. Clinical suggestions for the language assessment of Spanish-speaking children with different levels of English proficiency and research implications are discussed.

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STOCKMAL, VERNA, DANNY R. MOATES, and ZINNY S. BOND. "Same talker, different language." Applied Psycholinguistics 21, no. 3 (September 2000): 383–93. http://dx.doi.org/10.1017/s0142716400003052.

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When discriminating between unknown foreign languages, infants, young children, and adult listeners are able to make same-language/different-language discrimination judgments at better than chance levels. In these studies (Lorch & Meara, 1989; Mehler et al., 1988; Stockmal, 1995), foreign language samples have often been provided by different talkers, confounding voice characteristics and language characteristics. In Experiments 1 and 2, using the same talkers for different pairs of languages, we found that listeners were able to discriminate between languages they did not know, even when spoken by the same talker. That is, listeners were able to separate talker from language characteristics. Experiment 3 used multidimensional scaling to explore the bases of listener judgments. Listeners were attentive to prosodic properties and influenced by their familiarity with the test languages.

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Nelson, Nickola Wolf. "How to Code Written Language Samples for SALT Analysis." Perspectives of the ASHA Special Interest Groups 3, no. 1 (January 2018): 43–55. http://dx.doi.org/10.1044/persp3.sig1.43.

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Weston, Audrey D., Lawrence D. Shriberg, and Jon F. Miller. "Analysis of Language-Speech Samples with Salt and Pepper." Journal of Speech, Language, and Hearing Research 32, no. 4 (December 1989): 755–66. http://dx.doi.org/10.1044/jshr.3204.755.

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Microcomputer-aided analysis of spontaneous language-speech samples offers researchers an efficient means of analyzing large amounts of data. It may be necessary, however, to format samples for more than one software program in order to obtain comprehensive morpho-syntactic and phonetic/phonologic analyses. This paper suggests a procedure for the combined use of SALT (Systematic Analysis of Language Transcripts, Miller & Chapman, 1985) and PEPPER (Programs to Examine Phonetic and Phonologic Evaluation Records, Shriberg, 1986) that is designed to minimize the duplication of effort involved in following two different formatting procedures. Results of a study undertaken to explore methodological issues in the combined use of SALT and PEPPER generally support the validity, reliability, and efficiency of the procedure. Results also raise some issues concerning the use of narrow phonetic transcription as opposed to standard orthographic transcription of continuous language-speech samples.

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Dollaghan, Christine A., and Thomas F. Campbell. "A procedure for classifying disruptions in spontaneous language samples." Topics in Language Disorders 12, no. 2 (February 1992): 56–68. http://dx.doi.org/10.1097/00011363-199202000-00007.

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Meltzer, Jed A. "Towards early prediction of Alzheimer's disease through language samples." EClinicalMedicine 29-30 (December 2020): 100644. http://dx.doi.org/10.1016/j.eclinm.2020.100644.

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Senthil Kumar, K., and D. Malathi. "Context Free Grammar Identification from Positive Samples." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 1096. http://dx.doi.org/10.14419/ijet.v7i3.12.17768.

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In grammatical inference one aims to find underlying grammar or automaton which explains the target language in some way. Context free grammar which represents type 2 grammar in Chomsky hierarchy has many applications in Formal Language Theory, pattern recognition, Speech recognition, Machine learning , Compiler design and Genetic engineering etc. Identification of unknown Context Free grammar of the target language from positive examples is an extensive area in Grammatical Inference/ Grammar induction. In this paper we propose a novel method which finds the equivalent Chomsky Normal form.

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Evans, Julia L., and Holly K. Craig. "Language Sample Collection and Analysis." Journal of Speech, Language, and Hearing Research 35, no. 2 (April 1992): 343–53. http://dx.doi.org/10.1044/jshr.3502.343.

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Spontaneous language samples elicited during freeplay and interview contexts were compared for 10 children who were specifically language impaired (SLI). Clinician-child videotaped interactions were analyzed for both structural and conversational behaviors. The results indicated that the interview was a reliable and valid assessment context, eliciting the same profile of behaviors as the freeplay context without altering diagnostic classifications. Most behaviors occurred significantly more often during the interview than during the freeplay context, indicating further that interviews are an efficient language sampling alternative for assessment purposes with elementary school-aged children with language disorders.

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Heilmann, John, Thomas O. Malone, and Marleen F. Westerveld. "Properties of Spoken Persuasive Language Samples From Typically Developing Adolescents." Language, Speech, and Hearing Services in Schools 51, no. 2 (April 7, 2020): 441–56. http://dx.doi.org/10.1044/2019_lshss-19-00078.

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Purpose Persuasive communication skills are vital for achieving success in school, at work, and in social relationships. To facilitate assessment of persuasive discourse, we developed a clinically feasible persuasive speaking protocol and used it to compile a database of language samples. This database allowed us to describe the properties of adolescents' persuasive speaking skills. Method We collected spoken language samples from 179 typically developing students in Grades 8–12, recruited from the United States and Australia. Participants were asked to persuade an authority figure to make a change in a rule or policy. Results Language performance data reflecting both microstructural and macrostructural properties of spoken language were summarized and broken down by grade. We completed a factor analysis that documented three latent variables (syntax, discourse difficulties, and content). To test the validity of the persuasive measures, a subset of the participants completed an additional battery of assessments, which revealed weak to moderate relationships between the persuasive measures, general language ability, and working memory. There was no significant relationship between the persuasive language measures and an assessment of personality. Conclusion Our persuasive language sampling protocol facilitated the collection of valid language performance data. The summary data can be used as benchmarks for clinical evaluations of adolescents suspected of having language difficulties.

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Kovacs, Thomas, and Katya Hill. "Language Samples From Children Who Use Speech-Generating Devices: Making Sense of Small Samples and Utterance Length." American Journal of Speech-Language Pathology 26, no. 3 (August 15, 2017): 939–50. http://dx.doi.org/10.1044/2017_ajslp-16-0114.

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Purpose Mean length of utterance in morphemes (MLUm) is underreported in people who use augmentative and alternative communication (AAC). MLUm is difficult to measure in people who use AAC because of 2 challenges described in literature: the challenge of small language samples (difficulty collecting representative samples) and the challenge of transcribing short utterances (difficulty transcribing 1-morpheme utterances). We tested solutions to both challenges in a corpus of language samples from children who use speech-generating devices. Method The first challenge was addressed by adjusting the length of the sampling window to obtain representative language samples. The second challenge was addressed by using mean syntactic length (MSL) as an alternative to MLUm. Results A 24-hour sample window consistently failed to yield representative samples. An extended 1-month sample window consistently yielded representative samples. A significant positive prediction of MLUm by MSL was found in a normative sample. Observed measures of MSL were used to predict MLUm in representative language samples from children who use AAC. Conclusions Valid measures of utterance length in people who use AAC can be obtained using extended sampling windows and MSL. Research is needed to characterize the strengths and limitations of both solutions.

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Rojas, Raúl, and Aquiles Iglesias. "Using Language Sampling to Measure Language Growth." Perspectives on Language Learning and Education 17, no. 1 (March 2010): 24–31. http://dx.doi.org/10.1044/lle17.1.24.

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Abstract This article illustrates how speech-language pathologists (SLPs) can use language sampling and growth curve modeling (GCM) to examine the language growth rates of English Language Learners. GCM data on language samples provides SLPs with powerful, new tools to evaluate actual progress over time instead of relying on single, static measurement endpoints to determine typical development.

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Guo, Ling-Yu, and Sarita Eisenberg. "Sample Length Affects the Reliability of Language Sample Measures in 3-Year-Olds: Evidence From Parent-Elicited Conversational Samples." Language, Speech, and Hearing Services in Schools 46, no. 2 (April 2015): 141–53. http://dx.doi.org/10.1044/2015_lshss-14-0052.

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SOLORIO, T., M. SHERMAN, Y. LIU, L. M. BEDORE, E. D. PEÑA, and A. IGLESIAS. "Analyzing language samples of Spanish–English bilingual children for the automated prediction of language dominance." Natural Language Engineering 17, no. 3 (October 22, 2010): 367–95. http://dx.doi.org/10.1017/s1351324910000252.

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AbstractIn this work we study how features typically used in natural language processing tasks, together with measures from syntactic complexity, can be adapted to the problem of developing language profiles of bilingual children. Our experiments show that these features can provide high discriminative value for predicting language dominance from story retells in a Spanish–English bilingual population of children. Moreover, some of our proposed features are even more powerful than measures commonly used by clinical researchers and practitioners for analyzing spontaneous language samples of children. This study shows that the field of natural language processing has the potential to make significant contributions to communication disorders and related areas.

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Winke, Paula, Susan Gass, and Carol Myford. "THE RELATIONSHIP BETWEEN RATERS' PRIOR LANGUAGE STUDY AND THE EVALUATION OF FOREIGN LANGUAGE SPEECH SAMPLES." ETS Research Report Series 2011, no. 2 (December 2011): i—67. http://dx.doi.org/10.1002/j.2333-8504.2011.tb02266.x.

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Goldstein, G. "The Profile of Multiple Language Proficiencies: A Measure for Evaluating Language Samples of Deaf Children." Journal of Deaf Studies and Deaf Education 8, no. 4 (October 1, 2003): 452–63. http://dx.doi.org/10.1093/deafed/eng027.

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Pezold, Mollee J., Caitlin M. Imgrund, and Holly L. Storkel. "Using Computer Programs for Language Sample Analysis." Language, Speech, and Hearing Services in Schools 51, no. 1 (January 8, 2020): 103–14. http://dx.doi.org/10.1044/2019_lshss-18-0148.

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Purpose Although language sample analysis is widely recommended for assessing children's expressive language, few school-based speech-language pathologists routinely use it, citing a lack of time, resources, and training ( Pavelko, Owens, Ireland, & Hahs-Vaughn, 2016 ). The purpose of this clinical tutorial is (a) to describe options for language sample analysis using computer programs and (b) to demonstrate a process of using language sample analysis focused on the assessment of 2 preschool children as case studies. Method We provide an overview of collecting and analyzing child language samples and describe 3 programs for language sample analysis: 2 dedicated software programs (Computerized Language Analysis [ MacWhinney, 2000 ] and Systematic Analysis of Language Transcripts [ Miller & Iglesias, 2015 ]) and 1 protocol for using word processing software to analyze language samples (Sampling Utterances and Grammatical Analysis Revised; Pavelko & Owens, 2017 ). We also present analysis results from each program for play-based language samples from 2 preschool children and detailed analysis of the samples with potential treatment goals. Results Each program offers different analyses, comparison databases, and sampling contexts. We present options for additional analysis, clinical interpretations, and potential treatment goals based on the 2 preschool cases. Conclusion Clinicians can use computer programs for language sample analysis as part of a process to make naturalistic language assessment more feasible. Supplemental Material https://doi.org/10.23641/asha.10093403

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Owens, Robert E., and Stacey L. Pavelko. "Relationships Among Conversational Language Samples and Norm-Referenced Test Scores." Clinical Archives of Communication Disorders 2, no. 1 (April 30, 2017): 43–50. http://dx.doi.org/10.21849/cacd.2017.00052.

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Erin, Jane N. "Language Samples From Visually Impaired Four and Five-Year Olds." Journal of Childhool Communication Disorders 13, no. 2 (December 1990): 181–91. http://dx.doi.org/10.1177/152574019001300205.

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Bruckner, Cornelia Taylor, Paul J. Yoder, and R. A. McWilliam. "Generalizability and Decision Studies: An Example Using Conversational Language Samples." Journal of Early Intervention 28, no. 2 (January 2006): 139–53. http://dx.doi.org/10.1177/105381510602800205.

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Morris-Friehe, Mary J., and Dixie D. Sanger. "Language samples using three story elicitation tasks and maturation effects." Journal of Communication Disorders 25, no. 2-3 (April 1992): 107–24. http://dx.doi.org/10.1016/0021-9924(92)90002-e.

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Overton, Sarah, and Yvonne Wren. "Outcome measurement using naturalistic language samples: A feasibility pilot study using language transcription software and speech and language therapy assistants." Child Language Teaching and Therapy 30, no. 2 (February 14, 2014): 221–29. http://dx.doi.org/10.1177/0265659013519251.

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The ultimate aim of intervention for children with language impairment is an improvement in their functional language skills. Baseline and outcome measurement of this is often problematic however and practitioners commonly resort to using formal assessments that may not adequately reflect the child’s competence. Language sampling, transcription and analysis provide a more thorough and realistic picture of a child’s abilities but are time consuming and usually considered unfeasible in the typical clinic setting. This article reports on a pilot study in which a speech and language therapy assistant (SLTA) carried out language transcription using language analysis software. Following a brief initial training period, the SLTA carried out transcriptions on 17 language samples at two time periods. Reliability between a speech and language therapist and the assistant’s transcriptions were calculated and were found to be acceptable for the majority of measures taken. The use of software by assistants is proposed as a viable alternative for outcome measurement of naturalistic language skills.

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Goh, Ying Soon, Nasirin Abdillah, Noor Aileen Ibrahim, and Raja Mariam Raja Baniamin. "Language activities as a platform for second/foreign language learning : its relevance and challenges." Social and Management Research Journal 3, no. 2 (December 20, 2006): 97. http://dx.doi.org/10.24191/smrj.v3i2.5117.

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This study seeks to identify the relevance and challenges faced in conducting language activities to help foster second/foreign language learning at Universiti Teknologi MARA (UiTM) Terengganu. This is also aimed at assisting the organization to review practices of having language activities, hoping this may further cultivate and generate enthusiasm among students to learn second/foreign languages. This research also offers some possible ways to improve students' activities so as to help enhance their second/foreign language learning. In other words, this research may serve as a preliminary attempt aiming at providing reflections and insights on how students' participation in language activities conductedmay be usefulfor them in learning second/foreign languages. The research employs quantitative methodology in which random stratified sampling technique is used Samples consist of Bumiputera students taking English (as a second language) and Mandarin, French, and Arabic classes (as foreign languages). Generally, students are ofthe opinion that language activities can become a significant platform to project andshow their language skills and competency during presentations/performances. Nevertheless, as one ofmany recommendations suggested in the findings of this research, wefound that there is still a needfor evaluation andfo//ow-up work after having participated in the language activities.

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Mihaljević Djigunović, Jelena. "Language anxiety and language processing." EUROSLA Yearbook 6 (July 20, 2006): 191–212. http://dx.doi.org/10.1075/eurosla.6.12mih.

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This paper focuses on two studies into the effects of language anxiety on language processing. Using samples of Croatian L1 — English L2 speakers performing two picture description tasks (one in L1 and one in L2), the studies analysed their oral productions in order to identify a number of temporal and hesitation signals of planning processes. The findings suggest that observing learners using audio and video equipment and trying to increase their anxiety through interpersonal style does not produce a significant difference. However, learners watching someone apparently taking notes on their performance seemed to be significantly anxiety provoking. Qualitative analysis suggests that, in comparison with low anxiety language users, high anxiety language users produce longer texts in L2 than in L1, produce smaller amounts of continuous speech in both L1 and L2, produce filled pauses with a higher mean length in L2 than in L1, have longer mid-clause pauses, fewer repetitions, and make more false starts.

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Horton-Ikard, Ramonda, Susan Ellis Weismer, and Claire Edwards. "Examining the use of standard language production measures in the language samples of African-American toddlers." Journal of Multilingual Communication Disorders 3, no. 3 (January 2005): 169–82. http://dx.doi.org/10.1080/14769670500170768.

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O’Brien, Mary Grantham. "METHODOLOGICAL CHOICES IN RATING SPEECH SAMPLES." Studies in Second Language Acquisition 38, no. 3 (November 26, 2015): 587–605. http://dx.doi.org/10.1017/s0272263115000418.

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Much pronunciation research critically relies upon listeners’ judgments of speech samples, but researchers have rarely examined the impact of methodological choices. In the current study, 30 German native listeners and 42 German L2 learners (L1 English) rated speech samples produced by English-German L2 learners along three continua: accentedness, fluency, and comprehensibility. The goal was to determine whether rating condition, that is, (a) whether each speech sample is rated along all three continua after it is heard once or (b) whether all speech samples are rated along one continuum before being rated along the next continuum, and continuum order (e.g., whether participants rate speech samples for accentedness before comprehensibility or fluency) have an effect on listeners’ ratings. Results indicate no significant overall effects of rating condition or continuum order, but there is evidence of rating condition effects by listener group. The results have implications for laboratory and classroom assessments of L2 speech.

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Donohue, Mark. "Studying Contact without Detailed Studies of the Languages Involved: A Non-Philological Approach to Language Contact." Annual Meeting of the Berkeley Linguistics Society 38 (September 25, 2012): 92. http://dx.doi.org/10.3765/bls.v38i0.3324.

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<p>Studies of contact have revealed that all kinds of language material can, in the right circumstances, be borrowed from one language to another. Detecting, describing, and analyzing such situations typically involve the detailed study of at least two languages. An alternative involves detecting contact situations through database analysis. This cannot supplant the detailed work that requires detailed descriptive work in particular fields, but can allow us to examine large enough samples of languages that we can start to better understand, through calibration against known histories and other non-linguistic data types, likelihoods of different ‘social contact’ scenarios resulting in different kinds of linguistic traces, and also allow for the more targeted investigation of specific areas and language-to-language interactions. I shall describe the method, and illustrate its application in a number of case studies in regions for which we have good samples of language data.</p>

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Stavans, Ilan. "Don Quixote: Two Samples y un epitafio." TRANS. Revista de Traductología, no. 21 (December 16, 2017): 63. http://dx.doi.org/10.24310/trans.2017.v0i21.3731.

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Quick, Nancy, and Karen Erickson. "A Multilinguistic Approach to Evaluating Student Spelling in Writing Samples." Language, Speech, and Hearing Services in Schools 49, no. 3 (July 5, 2018): 509–23. http://dx.doi.org/10.1044/2018_lshss-17-0095.

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Purpose Spelling is a critical component of literacy and language arts that can negatively influence other aspects of written composition. This clinical focus article describes a spelling error classification system that can be used to identify underlying linguistic deficits that contribute to students' spelling errors. The system is designed to take advantage of the linguistic expertise of speech-language pathologists to efficiently assess student errors in written compositions that are generated as a component of everyday classroom instruction. Method A review of the literature was conducted regarding spelling as a component of literacy and language arts, the development of spelling, and the linguistic contributions to spelling. Then, existing criterion-referenced measures of spelling simple and morphologically complex words were reviewed, and a new, manual technique for analyzing spelling in student written compositions was created. Conclusions The language expertise of speech-language pathologists enables them to readily evaluate the phonological, orthographic, and morphological errors in student misspellings, in order to identify specific underlying linguistic deficits and plan targeted interventions. The error classification system provides speech-language pathologists with a tool that is both simple and time efficient and, thus, may help increase their confidence and ability in addressing the spelling needs of students.

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Milad, Kareema G., Yasser F. Hassan, and Ashraf S. El Sayed. "Multi-task Learning in Translating English Language into Arabic Language." International Journal of Emerging Research in Management and Technology 7, no. 5 (June 6, 2018): 4. http://dx.doi.org/10.23956/ijermt.v7i5.35.

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Machine learning techniques usually require a large number of training samples to achieve maximum benefit. In this case, limited training samples are not enough to learn models; recently there has been a growing interest in machine learning methods that can exploit knowledge from such other tasks to improve performance. Multi-task learning was proposed to solve this problem. Multi-task learning is a machine learning paradigm for learning a number tasks simultaneously, exploiting commonalities between them. When there are relations between the tasks to learn, it can be advantageous to learn all these tasks simultaneously instead of learning each task independently. In this paper, we propose translate language from source language to target language using Multi-task learning, for our need building a relation extraction system between the words in the texts, we applied related tasks ( part-of-speech , chunking and named entity recognition) and train it's in parallel on annotated data using hidden markov model, Experiments of text translation task show that our proposed work can improve the performance of a translation task with the help of other related tasks.

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Gromov, Vasilii A., and Anastasia M. Migrina. "A Language as a Self-Organized Critical System." Complexity 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/9212538.

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A natural language (represented by texts generated by native speakers) is considered as a complex system, and the type thereof to which natural languages belong is ascertained. Namely, the authors hypothesize that a language is a self-organized critical system and that the texts of a language are “avalanches” flowing down its word cooccurrence graph. The respective statistical characteristics for distributions of the number of words in the texts of English and Russian languages are calculated; the samples were constructed on the basis of corpora of literary texts and of a set of social media messages (as a substitution to the oral speech). The analysis found that the number of words in the texts obeys power-law distribution.

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Ebert, Kerry Danahy, and Giang Pham. "Synthesizing Information From Language Samples and Standardized Tests in School-Age Bilingual Assessment." Language, Speech, and Hearing Services in Schools 48, no. 1 (January 2017): 42–55. http://dx.doi.org/10.1044/2016_lshss-16-0007.

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Purpose Although language samples and standardized tests are regularly used in assessment, few studies provide clinical guidance on how to synthesize information from these testing tools. This study extends previous work on the relations between tests and language samples to a new population—school-age bilingual speakers with primary language impairment—and considers the clinical implications for bilingual assessment. Method Fifty-one bilingual children with primary language impairment completed narrative language samples and standardized language tests in English and Spanish. Children were separated into younger (ages 5;6 [years;months]–8;11) and older (ages 9;0–11;2) groups. Analysis included correlations with age and partial correlations between language sample measures and test scores in each language. Results Within the younger group, positive correlations with large effect sizes indicated convergence between test scores and microstructural language sample measures in both Spanish and English. There were minimal correlations in the older group for either language. Age related to English but not Spanish measures. Conclusions Tests and language samples complement each other in assessment. Wordless picture-book narratives may be more appropriate for ages 5–8 than for older children. We discuss clinical implications, including a case example of a bilingual child with primary language impairment, to illustrate how to synthesize information from these tools in assessment.

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Turetska, H. I., and B. I. Kunikevych. "UKRAINIAN LANGUAGE ADAPTATION OF THE MENTALIZATION QUESTIONNAIRE IN NON-CLINICAL SAMPLES." Habitus, no. 17 (2020): 131. http://dx.doi.org/10.32843/2663-5208.2020.17.23.

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Westerveld, Marleen F., and Kath Vidler. "Spoken language samples of Australian children in conversation, narration and exposition." International Journal of Speech-Language Pathology 18, no. 3 (April 13, 2016): 288–98. http://dx.doi.org/10.3109/17549507.2016.1159332.

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Westerveld, Marleen F., Gail T. Gillon, and Jon F. Miller. "Spoken language samples of New Zealand children in conversation and narration." Advances in Speech Language Pathology 6, no. 4 (January 2004): 195–208. http://dx.doi.org/10.1080/14417040400010140.

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Newman, Matthew L., Carla J. Groom, Lori D. Handelman, and James W. Pennebaker. "Gender Differences in Language Use: An Analysis of 14,000 Text Samples." Discourse Processes 45, no. 3 (May 15, 2008): 211–36. http://dx.doi.org/10.1080/01638530802073712.

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Kroecker, Jessica, Katherine Lyle, Katherine Allen, Erin Filippini, Melissa Galvin, Marc Johnson, Andrew Kanuck, et al. "Effect of Student Training on the Quality of Children's Language Samples." Contemporary Issues in Communication Science and Disorders 37, Spring (March 2010): 4–13. http://dx.doi.org/10.1044/cicsd_36_s_4.

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Bat-Hayim, MaryLouise. "Analyzing cohesion in language samples of college students: A diagnostic tool." Annals of Dyslexia 46, no. 1 (January 1996): 123–58. http://dx.doi.org/10.1007/bf02648174.

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