A New Method for Documenting Sign Language Productions in Schools

PURPOSE

There are well-established guidelines for the recording, transcription, and analysis of spontaneous oral language samples by researchers, educators, and speech pathologists. In contrast, there is presently no consensus regarding methods for the written documentation of sign language samples. The Handshape Analysis Recording Tool (HART) is an innovative method for documenting and analyzing word level samples of signed languages in real time. Fluent sign language users can document the expressive sign productions of children to gather data on sign use and accuracy.

METHOD

The HART was developed to document children's productions in Australian Sign Language (Auslan) in a bilingual-bicultural educational program for the Deaf in Australia. This written method was piloted with a group of fluent signing Deaf educational staff in 2014-2016, then used in 2022-2023 with a group of fluent signing professionals to examine inter- and intrarater reliability when coding parameters of sign accuracy.

RESULTS

Interrater reliability measured by Gwet's Agreement Coefficient, was "good" to "very good" across the four phonological parameters that are components of every sign: location, movement, handshape, and orientation.

CONCLUSIONS

The findings of this study indicate that the HART can be a reliable tool for coding the accuracy of location, orientation, movement, and handshape parameters of Auslan phonology when used by professionals fluent in Auslan. The HART can be utilized with any sign language to gather word level sign language samples in a written form and document the phonological accuracy of signed productions.

Growth of Complex Syntax: Coordinate and Subordinate Clause Use in Elementary School-Aged Children

PURPOSE

Production of complex syntax is a hallmark of later language development; however, most of the research examining age-related changes has focused on adolescents or analyzed narrative language samples. Research documenting age-related changes in the production of complex syntax in elementary school-aged children in conversational language samples is limited. Therefore, the purpose of this article is to examine age-related changes in the production of coordinate and subordinate clauses in children between 5 and 10 years of age obtained from 50-utterance conversational language samples.

METHOD

The analytic sample included 196 children with typical language development, who ranged in age from 5;0 to 10;11 (years;months; girls = 103; boys = 96; three cases were excluded). Fifty-utterance conversational language samples were examined for use of coordinate and subordinate clauses.

RESULTS

Results of regression analyses indicated that the production of coordinate and subordinate clauses could be predicted from age. The proportion of utterances that included subordinate clauses increased 0.20% for every month increase in age (p < .001). Coordinate clauses also continued to grow, although at a slower rate (0.10% increase for every month increase in age, p < .001). Finally, the proportion of simple utterances (i.e., utterances without coordinate or subordinate clauses) decreased with age (0.40% decrease for every month increase in age, p < .001).

CONCLUSIONS

This study indicated that as children's age increased, they used fewer, simple, one-clause sentences and more utterances that included subordinate clauses, with or without coordinate clauses. These results were obtained from 50-utterance language samples, further supporting use of language sampling to develop intervention goals and monitor progress in therapy.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25262725.

Transcription Decisions of Conjoined Independent Clauses Are Equitable Across Dialects but Impact Measurement Outcomes

PURPOSE

Transcription of conjoined independent clauses within language samples varies across professionals. Some transcribe these clauses as two separate utterances, whereas others conjoin them within a single utterance. As an inquiry into equitable practice, we examined rates of conjoined independent clauses produced by children and the impact of separating these clauses within utterances on measures of mean length of utterance (MLU) by a child's English dialect, clinical status, and age.

METHOD

The data were archival and included 246 language samples from children classified by their dialect (African American English or Southern White English) and clinical status (developmental language disorder [DLD] or typically developing [TD]), with those in the TD group further classified by their age (4 years [TD4] or 6 years [TD6]).

RESULTS

Rates of conjoined independent clauses and the impact of these clauses on MLU varied by clinical status (DLD < TD) and age (TD4 < TD6), but not by dialect. Correlations between the rate of conjoined clauses, MLU, and language test scores were also similar across the two dialects.

CONCLUSIONS

Transcription decisions regarding conjoined independent clauses within language samples lead to equitable measurement outcomes across dialects of English. Nevertheless, transcribing conjoined independent clauses as two separate utterances reduces one's ability to detect syntactic differences between children with and without DLD and document syntactic growth as children age.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25822675.

Maximizing Student Outcomes in Schools: Data-Driven Individualized Education Program Goals and Objectives Aligned to the Standards

PURPOSE

Federal and state laws require schools to be accountable for student performance on measures of academic achievement in literacy, mathematics, and science skills; monitor high school graduation rates; and track student growth and academic progression throughout the grade levels. Success on these measures gives students pathways to postsecondary options in the workforce, technical education, or college/university education. Speech-language pathologists (SLPs) can utilize existing data sources in the school in conjunction with their knowledge of diagnostics and treatment of cognition, language, and culture to maximize student outcomes beyond the therapy room and in the curriculum standards.

METHOD

This clinical focus article will review methods of data collection from existing sources in the students' grade level and academic content curriculum. Utilization of information and input from parents, teachers, paraprofessionals, and others who support the student's academic endeavors will be discussed for Individualized Education Program Present Levels of Academic Achievement and Functional Performance and goal development. Procedures for the SLP to collect independent data aligned to standards-based curriculum will be explored. Consideration will be given for utilizing data to develop future goals and objectives. The benefits of data collection for monitoring student progress and success in the grade-level or alternate standards will be highlighted through case examples. Techniques for classroom-based services, model lessons, coaching, and co-teaching will be presented.

CONCLUSIONS

SLPs are recognized as specialized instructional support personnel who have unique knowledge and expertise for diagnosing and treating speech/language impairments (S/LI) disabilities. By understanding how they can use existing data and apply it to developing intervention plans, SLPs can ensure students with S/LI disabilities experience the maximum potential for outcomes in academic and functional success. Data-driven tools and methods aligned to the grade-level standards and curriculum are one of the most powerful tools in an SLP's toolbox.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.24869592.

Measuring productive syntactic abilities in Mandarin-speaking children in Taiwan

PURPOSE

This study aimed to develop a fine-grained measure for evaluating syntactic abilities in Mandarin-speaking children for educational and clinical purposes as a supplement to MLU.

METHOD

In total, 99 typically developing children, aged 2;0 to 5;11, living in Taipei, Taiwan, participated in this study. Spontaneous language samples were elicited in free-play situations. The first 100 intelligible utterances were coded with a newly developed scheme: the Mandarin Assessment of Productive Syntax-Revised (MAPS-R). For the examination of concurrent validity, MLU was also computed.

RESULTS

Significant age-related differences were observed in both MLU and MAPS-R scores. Strong correlations were found between MLU and MAPS-R scores, confirming the validity of MAPS-R as a measure of syntactic development. MAPS-R further revealed that Mandarin-speaking children expanded noun phrases with the general classifier 'GE' very early on, followed by a locative expression. Verb expansions began with resultative complements and aspect markers. Sentences with complex predication structures, such as serial verbs/pivotal sentences, were still not widely used when the MLU value is below 4.5.

CONCLUSIONS

The study showed that MAPS-R is a reliable and valid measure that can provide a rich profile of the syntactic development of Mandarin-speaking children. It can be a useful reference for speech therapists to set a baseline for developing language intervention plans and to monitor their outcome.

Assessing Narrative Microstructure in Mandarin-Speaking School-Age Children

PURPOSE

As a representative form of discourse, oral narratives offer a sensitive, flexible, and ecologically valid tool for language evaluation. Nevertheless, oral narrative assessments in mainland China remain uncommon among school-age children. Therefore, this study explores the effectiveness of narrative microstructural assessment in Mandarin-speaking school-age children. Its primary purpose is to lay a foundation for future clinical applications by establishing a feasible administration procedure and validating various viable measures to facilitate the evaluation of language proficiencies.

METHOD

Narrative samples were collected through a single wordless picture from 285 Mandarin-speaking children ages 7-12 years (Grades 1-6). The samples were then coded using a semiautomatic method for 22 individual measures at word, sentence, and discourse levels.

RESULTS

The adapted elicitation method was effective for all grades, with most of the children's stories exhibiting sufficient length, coherent structure, and certain self-fabricated specifics. The semiautomatic coding was also convenient and promising. Altogether, 19 measures were identified as robust predictors of grades, revealing a general tendency of grade-related increase from Grades 1 to 5. These measures exhibited three representative nonlinear trajectory patterns with varied rates of increase. Then, we developed a comprehensive composite measure through factor analysis, which combined six individual measures. The analysis verified the single factor of narrative microstructural competence and showed strong evidence of construct validity.

CONCLUSIONS

This study has provided a practical set of administration procedures, validated the efficacy of 19 individual measures as well as a composite measure, and offered corresponding field test data for clinical matching. The findings underscored the reliability and informative nature of oral narrative microstructural assessment in Mandarin-speaking school-age children.

The characteristics of spontaneous language in young children identified as language delayed in Mandarin

BACKGROUND

Little is known about the spontaneous speech characteristics of young children with language delay in Mandarin, relative to their peers. Until the recent development of standardized language assessments normed in China on Mandarin-speaking children, it was difficult to independently identify atypically developing children to study their spontaneous speech, and only case studies have been available.

AIM

To investigate which aspects of spontaneous speech might be distinctive for atypical development in a short play session.

METHODS & PROCEDURES

A total of 86 Mandarin-speaking children, boys and girls aged 2;6-4;6, were tested using the new standardized assessment for Mandarin, Diagnostic Receptive Expressive Assessment of Mandarin-Comprehensive (DREAM-C), at a major urban hospital in China. Of the children, 39 were identified by DREAM-C as atypically developing in language development (Total Standard score M = 72, SD = 8.9), and 47 scored in the typical range (Total Standard score M = 103, SD = 10.8), using the four scales of Receptive, Expressive Semantics and Syntax. All children then took part in a 15-min semi-structured play session during which their spontaneous speech was recorded by professionals. A variety of games and pictures were used in an attempt to elicit spontaneous questions, negatives and descriptions. Their recorded speech samples were then coded by linguists directly into a database in FilemakerPro for different aspects of vocabulary, sentence variety and grammatical morphemes/structures heard.

OUTCOMES & RESULTS

The results describe the characteristics of the speech samples for the typical and atypical groups for age groups 2;6-3.5 (N = 52) and 3;6-4.6 (N = 34). Vocabulary diversity was indicated on an ordinal scale ranging from simple communicative signals including headshakes and words such as 'hi' to 'a rich variety of different content words'. Grammatical diversity similarly ranged from 'only yes/no answers', through to the appearance of multi-clause sentences. Morphosyntax was coded in terms of which morphemes were observed at all in the session, such as aspect markers (LE, ZAI, GUO), and nominal morphemes (DE, GE), and also whether function words such as pronouns, Wh-questions and classifiers were singular or varied in the session. There is considerable optionality in morpheme expression in Mandarin, so measurements such as the percentage supplied in obligatory contexts, though useful for languages such as English, are harder to compute. Nevertheless, the data show change over age in all these aspects of language, and reveal what a typically developing child might be expected to produce in a 15-min sample in such a session. For example, it was rare for the typically developing children by age 3.5 not to have at least simple sentences with some function words, and to use adjectives, nouns and verbs, unlike the children with atypical scores. The morphemes DE, LE and GUO showed significant differences in likelihood across groups for both ages, but BA and ZAI were significantly different only for the older age group. In contrast, GE was common in all groups. The atypical group has markedly lower frequency in several grammatical aspects such as the use of diverse questions, classifiers and pronouns, with much less change across age groups, implying slower growth. The results provide useful information on the relative likelihood of observing different varieties of words, sentence types and morphemes in a short speech sample, which are substantially different in the typical versus atypical groups in both age bands, and change over age.

CONCLUSIONS & IMPLICATIONS

These data validate the DREAM-C classifications, but the details can also be used to inform the choice of targets for intervention for young children who experience delays in Mandarin language acquisition.

WHAT THIS PAPER ADDS

What is already known on the subject Spontaneous language samples have been used as a means of studying language in China. However, because of the amount of training and time required to transcribe and analyse spontaneous language samples, there is not yet a sufficient basis for identifying language differences between children with and without language disorder in mainland China. What does this paper add to existing knowledge After using DREAM-C to provide an objective measurement of children with and without language disorders, an easy-to-administer spontaneous language assessment protocol and scoring record form allowed the comparison of the spontaneous language of 39 children with atypically developing language with 47 children who scored in the typical range to observe language differences between those with and without typical language development. What are the potential or actual clinical implications of this work? The study documents differences in vocabulary and sentence variety, and use of different morphemes such as classifiers, passives and aspect markers to aid in choosing targets for intervention by demonstrating the path of development. In addition, the spontaneous language assessment protocol and scoring record form holds promise for allowing clinicians and researchers to more easily study the language of individual children to personalize intervention, but also of groups of children to understand the emergence of basic Mandarin linguistic features.

UK speech and language therapists' assessment of children's expressive language, and functional impairment and impact, following the CATALISE publications

BACKGROUND

In 2016/17, the CATALISE Consortium published the results of a multinational and multidisciplinary Delphi consensus study, representing agreement among professionals about the definition and process of identification of children with Developmental Language Disorder (DLD) (Bishop et al., 2016, 2017). The extent to which the current clinical practice of UK speech and language therapists (SLTs) reflects the CATALISE consensus statements is unknown.

AIMS

To investigate how UK SLTs' expressive language assessment practices reflect the CATALISE documents' emphasis on the functional impairment and impact caused by DLD, by examining: whether multiple sources of assessment information are gathered; how standardised and non-standardised sources are combined in clinical decision-making, and how clinical observation and language sample analysis are utilised.

METHODS AND PROCEDURES

An anonymous, online survey was carried out between August 2019 and January 2020. It was open to UK-based paediatric SLTs who assess children up to age 12 with unexplained difficulties using language. Questions probed different aspects of expressive language assessment which are referred to in the CATALISE consensus statements and supplementary comments, and asked about participants' familiarity with the CATALISE statements. Responses were analysed using simple descriptive statistics and content analysis.

OUTCOMES AND RESULTS

The questionnaire was completed by 104 participants, from all four regions of the United Kingdom, working in a range of clinical settings with different levels of professional experience of DLD. The findings indicate that clinical assessment practices broadly align with the CATALISE statements. Although clinicians carry out standardised assessments more frequently than other types of assessment, they also gather information from other sources and use this alongside standardised test scores to inform clinical decisions. Clinical observation and language sample analysis are commonly utilised to evaluate functional impairment and impact, along with parent/carer/teacher and child report. However, asking about the child's own perspective could be more widely utilised. The findings also highlight a lack of familiarity with the details of the CATALISE documents among two thirds of the participants.

CONCLUSIONS AND IMPLICATIONS

Assessment practices broadly align with the CATALISE statements, but there is a need for greater clarity regarding terminology and the assessment of functional language impairment and impact. This research should prompt discussion in the profession about how to further develop and adopt expressive language assessment practices which reflect the CATALISE consensus and support effective assessment.

WHAT THIS PAPER ADDS

What is already known on the subject The CATALISE consortium documents on Developmental Language Disorder (DLD) were published in 2016/17. The extent to which expressive language assessment practice in the United Kingdom reflects the new definition and statements on assessment has not previously been investigated. What this paper adds to existing knowledge This survey indicates that speech and language therapists in the United Kingdom assessing children for DLD mostly balance standardised language test scores with other sources of information in clinical decision-making, and utilise clinical observation and language sample analysis to consider functional impairment and the impact of the language disorder. However, important questions are raised regarding the robustness and objectivity with which these key parameters are currently defined and evaluated. What are the potential or actual clinical implications of this work? Clinicians, individually and at service level, are encouraged to reflect on their assessment of functional impairment and the impact of language disorder and to take steps to incorporate this where necessary. Professional guidance and clinical tools to facilitate robust, objective assessment would support clinical practice that aligns with expert consensus.

Automation of Language Sample Analysis

PURPOSE

A major barrier to the wider use of language sample analysis (LSA) is the fact that transcription is very time intensive. Methods that can reduce the required time and effort could help in promoting the use of LSA for clinical practice and research.

METHOD

This article describes an automated pipeline, called Batchalign, that takes raw audio and creates full transcripts in Codes for the Human Analysis of Talk (CHAT) transcription format, complete with utterance- and word-level time alignments and morphosyntactic analysis. The pipeline only requires major human intervention for final checking. It combines a series of existing tools with additional novel reformatting processes. The steps in the pipeline are (a) automatic speech recognition, (b) utterance tokenization, (c) automatic corrections, (d) speaker ID assignment, (e) forced alignment, (f) user adjustments, and (g) automatic morphosyntactic and profiling analyses.

RESULTS

For work with recordings from adults with language disorders, six major results were obtained: (a) The word error rate was between 2.4% for controls and 3.4% for patients, (b) utterance tokenization accuracy was at the level reported for speakers without language disorders, (c) word-level diarization accuracy was at 93% for control participants and 83% for participants with language disorders, (d) utterance-level diarization accuracy based on word-level diarization was high, (e) adherence to CHAT format was fully accurate, and (f) human transcriber time was reduced by up to 75%.

CONCLUSION

The pipeline dramatically shortens the time gap between data collection and data analysis and provides an output superior to that typically generated by human transcribers.

Describing the Spoken Language Skills of Typically Developing Afrikaans-Speaking Children Using Language Sample Analysis: A Pilot Study

PURPOSE

Language sample analysis is widely regarded as the gold standard of language assessment. However, the uncertainty regarding the optimal length of sample and the limited availability of developmental language data for nonmainstream languages such as Afrikaans complicate reliable use of the method. The study aimed to provide guidelines on representative length of sample and concurrently provide a preliminary description of the spoken language skills of Afrikaans-speaking children.

METHOD

The study involved 30 typically developing Afrikaans-speaking children aged between 3;6 and 9;6 (years;months). A descriptive research design was used to transcribe and analyze 1-hr interactions collected in natural environments of participants who were recruited using referral sampling. Video and audio recordings of the samples were transcribed using adapted Sampling Utterances and Grammatical Analysis Revised analysis procedures.

RESULTS

Results indicated that mean length of utterance in words per minute, number of different words per minute, and total number of words per minute stabilized at 30 min and no significant differences were found between 30 min and longer time segments. Morphology results concur with existing developmental findings in Afrikaans. Lexical diversity results correlated with the findings of the lexical specificity and accuracy in the Prutting and Kirchner Pragmatic Protocol (Prutting & Kirchner, 1987). The developmental trajectories for pragmatic and phonological development were consistent with existing guidelines.

CONCLUSIONS

The study concluded that a 30-min interaction segment provides a representative language sample for Afrikaans-speaking children who are between 3;6 and 9;6. It provides promising preliminary developmental data and clinical guidelines, confirming the potential of language sample analysis (LSA) as a reliable component of language assessment in Afrikaans.

Assessment and Therapy Goal Planning Using Free Computerized Language Analysis Software

Background

We discuss a free software system (Computerized Language Analysis [CLAN]) that can enable fast, thorough, and informative language sample analysis (LSA).

Method

We describe methods for eliciting, transcribing, analyzing, and interpreting language samples. Using a hypothetical child speaker, we illustrate use KidEval to generate a diagnostic report.

Results

Given LSA results suggestive of expressive language delay, we analyze further using CLAN's Developmental Sentence Score and Index of Productive Syntax routines, and outline the child's use of Brown's morphemes.

Discussion

This tutorial provides an introduction to the use of free CLAN software. We discuss how LSA results can be used to structure therapy goals that address specific aspects of grammatical structure that the child may not yet demonstrate in their spoken language. Finally, we provide answers to common questions, including user support.

Syntactic Complexity Across Language Sampling Contexts in School-Age Children, Ages 8-11 Years

PURPOSE

Syntax has been called the structural foundation of language, as its development allows for more efficient and effective communication. Complex syntax production is known to lag in children and adolescents with language impairment. Conversation, narrative, and expository language sampling contexts are recommended tools for the comprehensive assessment of school-age children, including syntactic abilities. Despite these recommendations, few studies have examined syntactic differences obtained from these three sampling contexts in a within group sample of school-age children, (i.e., ages 8-11 years). Information about similarities and differences in syntactic measures obtained from these three sampling contexts is needed to identify the optimal sampling context(s) for eliciting complex syntax.

METHOD

Conversational, narrative, and expository language samples were elicited from 85 children with typically developing language ages 8;0-11;11 (years;months). Samples were transcribed and analyzed for the mean length of utterance in words and clausal density, or the number of clauses per communication unit as measured by the subordination index.

RESULTS

Syntactic measures differed significantly across the three sampling contexts. Namely, narrative and expository language samples elicited longer utterances and more syntactically complex language than conversation samples. Age-related differences in the syntactic measures were not detected.

CONCLUSIONS

Differences in syntactic measures between conversation and narrative samples and conversation and expository samples within children ages 8-11 years support use of narrative and expository contexts as the most appropriate language sampling contexts for elicitation of complex syntax in school-age children ages 8-11 years. Conversation sampling is unlikely to elicit children's capacities for complex sentence production.

The Evolution of an Innovative Online Task to Monitor Children's Oral Narrative Development

Oral narrative abilities are an important measure of children's language competency and have predictive value for children's later academic performance. Research and development underway in New Zealand is advancing an innovative online oral narrative task. This task uses audio recordings of children's story retells, speech-to-text software and language analysis to record, transcribe, analyse and present oral narrative and listening comprehension data back to class teachers. The task has been designed for class teachers' use with the support of SLP or literacy specialists in data interpretation. Teachers are upskilled and supported in order to interpret these data and implement teaching practices for students through online professional learning and development modules, within the context of a broader evidence-based approach to early literacy instruction. This article describes the development of this innovative, culturally relevant, online tool for monitoring children's oral narrative ability and listening comprehension in their first year of school. Three phases of development are outlined, showing the progression of the tool from a researcher-administered task during controlled research trials, to wide-scale implementation with thousands of students throughout New Zealand. The current iteration of the tool uses an automatic speech-recognition system with specifically trained transcription models and support from research assistants to check transcription, then code and analyse the oral narrative. This reduces transcription and analysis time to ~7 min, with a word error rate of around 20%. Future development plans to increase the accuracy of automatic transcription and embed basic language analysis into the tool, with the aim of removing the need for support from research assistants.

Tutorial: Data Collection and Documentation Strategies for Speech-Language Pathologist/Speech-Language Pathology Assistant Teams

Purpose:

This tutorial outlines an approach for best practices for speech-language pathology assistants (SLPAs) to collect data and document services. The tutorial outlines methods for developing accurate and effective data collection skills and provides instructions and tools for collecting various types of data. The authors discuss the importance of data collection as it relates to the roles and responsibilities of speech-language pathologists (SLPs) and SLPAs. The process of planning for data collection, recording the data, and reporting data to the supervising SLP is outlined.

Conclusion:

Developing competence with a wide variety of data collection methods, data types, and documentation tools will assist SLPAs to accurately communicate the results of the services they provide, therefore enhancing the SLP's ability to make well-informed clinical decisions for the student.

Supplemental Material:

https://doi.org/10.23641/asha.20277726

The Reliability of Short Conversational Language Sample Measures in Children With and Without Developmental Language Disorder

PURPOSE

Language sample analysis (LSA) represents an ecologically valid method for diagnosing, identifying goals, and measuring progress in children with developmental language disorder (DLD). LSA is, however, time consuming. The purpose of this study was to determine the length of sample needed to obtain reliable LSA measures for children in kindergarten and first grade with typical language (TL) and DLD using automated analyses from the Systematic Analysis of Language Transcripts software.

METHOD

Play-based conversational language samples collected on kindergarten to first-grade children with TL (n = 21) and DLD (n = 21) from a community-based sample were analyzed. Eight LSA measures were calculated from 1-, 3-, 5-, 7-, and 10-min sample cuts and compared to 20-min samples for reliability.

RESULTS

Reliability estimates were similar for the TL and DLD groups except for errors and omissions, which showed overall higher levels of reliability in the DLD group and reached acceptable levels at 3 min. Percent grammatical utterances were reliable at 7 min in the DLD group and not reliable in shorter samples in the TL group. The subordination index was reliable at 10 min for both groups. Number of different words reached acceptable reliability at the 3-min length for the DLD group and at the 10-min length for the TL group. Utterances and words per minute were reliable at 3 min and mean length of utterance at 7 min in both groups.

CONCLUSIONS

Speech-language pathologists can obtain reliable LSA measures from shorter, 7-min conversational language samples from kindergarten to first-grade children with DLD. Shorter language samples may encourage increased use of LSA.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.19529287.

Dynamic Norming and Open Science

In a recent issue of JSLHR, Tucci et al. (2022) presented a method for assigning SEM scores to a language sample. However, this method is based on data that are not publicly available and uses a commercial analysis program that is not open source. The TalkBank system and the Child Language Data Exchange System database provides free analysis software based on openly accessible data, thereby adhering to Open Science standards, which represent an important next step for the fields of speech and hearing.

The Adaptation of Developmental Sentence Scoring and Index of Productive Syntax to Turkish

PURPOSE

Language sampling analysis (LSA) plays an important role in evaluating language skills; hence, the study aimed to develop new assessment measures for the LSA in Turkish as alternatives to mean length of utterance (MLU) and the Language Assessment, Remediation and Screening Procedure. With this aim, Developmental Sentence Scoring (DSS) and the Index of Productive Syntax (IPSyn) were adapted to Turkish.

METHOD

Eighty monolingual Turkish children were included in the study, and the age range was from 2;0 to 5;11 (years;months). The children were grouped with 6-month intervals, and each group had an equal number of participants in terms of gender. Their general and language development were tested with standardized tests, and language sampling was recorded during play with farm toys for 15-20 min. Reviewing literature and observing participants' production schemas were created for DSS for Turkish (DSS-TR) and the IPSyn for Turkish (IPSyn-TR) separately, and final versions were determined in consultation with experts.

RESULTS

DSS-TR and IPSyn-TR were significantly correlated with standardized tests, and MLU values were statistically significant (p < .05). Total scores increased with age; however, grammatical categories did not go up. No difference was observed between genders (p > .05). In DSS-TR, the "sentence point" did not affect the participants' total scores because of language characteristics (p > .05). Finally, DSS-TR and IPSyn-TR were seen to be correlated with each other (p < .05).

CONCLUSIONS

DSS-TR and IPSyn-TR are valid, being correlated with other assessment tools, and reliable, showing a high correlation with other raters, to reflect morphosyntactic skills. Therefore, they both are alternative assessment measures that will be used in LSA and give an opportunity to clinicians to plan their intervention goals. Also, they enable clinicians to observe progress not only specific to grammatical category but also in the total scores of the children either during or at the end of the therapy.

Predicting Language Performance From Narrative Language Samples

PURPOSE

Analysis of narrative language samples is a recommended clinical practice in the assessment of children's language skills, but we know little about how results from such analyses relate to overall oral language ability across the early school years. We examined the relations between language sample metrics from a short narrative retell, collected in kindergarten, and an oral language factor in grades kindergarten through 3. Our specific questions were to determine the extent to which metrics from narrative language sample analysis are concurrently related to language in kindergarten and predict language through Grade 3.

METHOD

Participants were a sample of 284 children who were administered a narrative retell task in kindergarten and a battery of vocabulary and grammar measures in kindergarten through Grade 3. Language samples were analyzed for number of different words, mean length of utterance, and a relatively new metric, percent grammatical utterances (PGUs). Structural equation models were used to estimate the concurrent and longitudinal relationships.

RESULTS

The narrative language sample metrics were consistently correlated with the individual vocabulary and grammar measures as well as the language factor in each grade, and also consistently and uniquely predicted the language factor in each grade. Standardized path estimates in the structural equation models ranged from 0.20 to 0.39.

CONCLUSIONS

This study found narrative language sample metrics to be predictive, concurrently and longitudinally, of a latent factor of language from kindergarten through Grade 3. These results further validate the importance of collecting and analyzing narrative language samples, to include PGU along with more traditional metrics, and point to directions for future research.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.17700980.

Dynamic Norming for Systematic Analysis of Language Transcripts

PURPOSE

This exploratory study sought to establish the psychometric stability of a dynamic norming system using the Systematic Analysis of Language Transcripts (SALT) databases. Dynamic norming is the process by which clinicians select a subset of the normative database sample matched to their individual client's demographic characteristics.

METHOD

The English Conversation and Student-Selected Story (SSS) Narrative databases from SALT were used to conduct the analyses in two phases. Phase 1 was an exploratory examination of the standard error of measure (SEM) of six clinically relevant transcript metrics at predetermined sampling intervals to determine (a) whether the dynamic norming process resulted in samples with adequate stability and (b) the minimum sample size required for stable results. Phase 2 was confirmatory, as random samples were taken from the SALT databases to simulate clinical comparison samples. These samples were examined (a) for stability of SEM estimations and (b) to confirm the sample size findings from Phase 1.

RESULTS

Results of Phase 1 indicated that the SEMs for the six transcript metrics across both databases were low relative to each metric's scale. Samples as small as 40-50 children in the Conversation database and 20-30 children in the SSS Narrative database resulted in stable SEM estimations. Phase 2 confirmed these findings, indicating that age bands as small as ±4 months from a given center-point resulted in stable estimations provided there were approximately 35 children or more in the comparison sample.

CONCLUSION

Psychometrically stable comparison samples can be achieved using SALT's dynamic norming system that are much smaller than the standard sample size recommended in most tests of children's language.

An Evaluation of Expedited Transcription Methods for School-Age Children's Narrative Language: Automatic Speech Recognition and Real-Time Transcription

Purpose This study examined the accuracy and potential clinical utility of two expedited transcription methods for narrative language samples elicited from school-age children (7;5-11;10 [years;months]) with developmental language disorder. Transcription methods included real-time transcription produced by speech-language pathologists (SLPs) and trained transcribers (TTs) as well as Google Cloud Speech automatic speech recognition. Method The accuracy of each transcription method was evaluated against a gold-standard reference corpus. Clinical utility was examined by determining the reliability of scores calculated from the transcripts produced by each method on several language sample analysis (LSA) measures. Participants included seven certified SLPs and seven TTs. Each participant was asked to produce a set of six transcripts in real time, out of a total 42 language samples. The same 42 samples were transcribed using Google Cloud Speech. Transcription accuracy was evaluated through word error rate. Reliability of LSA scores was determined using correlation analysis. Results Results indicated that Google Cloud Speech was significantly more accurate than real-time transcription in transcribing narrative samples and was not impacted by speech rate of the narrator. In contrast, SLP and TT transcription accuracy decreased as a function of increasing speech rate. LSA metrics generated from Google Cloud Speech transcripts were also more reliably calculated. Conclusions Automatic speech recognition showed greater accuracy and clinical utility as an expedited transcription method than real-time transcription. Though there is room for improvement in the accuracy of speech recognition for the purpose of clinical transcription, it produced highly reliable scores on several commonly used LSA metrics. Supplemental Material https://doi.org/10.23641/asha.15167355.

Oral and Written Language Abilities of School-Age Internationally Adopted Children from Eastern Europe

PURPOSE

Children adopted from Eastern Europe were assessed at ages 6 to 7 years and results were compared to the same children when they were 8 years. Patterns of relative strengths and weaknesses in language, verbal memory and literacy were analyzed. Variables that predicted reading and writing were determined.

METHOD

Children adopted from Eastern Europe between 1;0 and 4;11 years of age were assessed at ages 6 to 7 years and age 8 years on a variety of tests that measured language, verbal memory and literacy. Results were compared across ages, and language, verbal memory and literacy domains.

RESULTS

Group means for all measures fell within the average range at both ages. The children's scores were not significantly different from test norms except for measures of rapid naming and number repetition. However, a larger than expected percentage of children scored -1SD below the mean on decontextualized measures of verbal working memory and reading fluency. At age 8 years 24% of children received speech language therapy services and 26% had repeated a grade level. Vocabulary, expressive syntax, verbal short-term memory and writing were areas of relative strength. Higher level vocabulary knowledge was strongly correlated with all literacy measures.

CONCLUSION

As a group, Eastern European adoptees scored average on measures of language and literacy at 6 or 7 years and again at age 8 years. However, 26% of the children had repeated a grade and 24% were still receiving speech and language services. Vocabulary was an area of strength reflecting the children's enriched adopted home environments. In-depth knowledge of vocabulary was the best predictor of reading and writing. Some aspects of working memory were a strength but others were not. Rapid naming was also a weakness.

Development of narratives in Tamil-speaking preschool children: A task comparison study

‘Narrative’ can be simply defined as a spoken or written account of connected events or experiences. The present study records the development of microstructure elements of narratives in 200 typically developing Tamil-speaking children aged between three years and six years and eleven months. It then compares their narrative productivity across two elicitation contexts: story retelling (SR) and story generation (SG). The samples thus obtained are analyzed for three narrative microstructure parameters, namely total number of words (TNW) in the narrative, mean length of utterances (MLU) and the number of utterances. The results reveal an increasing trend in all three microstructure parameters across both contexts. All three parameters are found to be quantitatively high in SR than in SG. Variation in the performance in these narrative tasks has been explained with behavioural observations from literature, cognitive architecture and a working memory model. It was found that gender differences do not follow a uniform pattern across age groups and elicitation contexts. Since the study has generated normative data for microstructure parameters of narratives, the observations can be used to analyze language deviance and help plan the narrative intervention protocol for language therapy.

Nonverbal Intelligence Predicts Performance of Preschool Children With Hearing Loss on Norm-Referenced Language Measures But Not Language Sample Analysis

OBJECTIVES

The purpose of this study was to explore the effects of nonverbal intelligence on spoken language performance in children with hearing loss who use amplification and spoken language. Specifically, we compared language performance measured by norm-referenced measures compared with spontaneous language sample measures.

DESIGN

Nonverbal intelligence and spoken language skills were analyzed using norm-referenced nonverbal intelligence and language assessments and language sample analyses in 92 children ages 3 to 6 who varied by hearing and nonverbal intelligence status. All children had nonverbal intelligence within the average or above-average range.

RESULTS

Regression analyses revealed that, after controlling for maternal education and hearing status, nonverbal intelligence contributed unique variance to norm-referenced language measures but not spontaneous language measures.

CONCLUSIONS

Performance on norm-referenced language measures is influenced by nonverbal intelligence, whereas functional language use measured by spontaneous language samples is not. These findings suggest that spontaneous language measures may contribute additional value to language assessment batteries that are independent of nonverbal intelligence.

Using Free Computer-Assisted Language Sample Analysis to Evaluate and Set Treatment Goals for Children Who Speak African American English

Purpose Spoken language sample analysis (LSA) is widely considered to be a critical component of assessment for child language disorders. It is our best window into a preschool child's everyday expressive communicative skills. However, historically, the process can be cumbersome, and reference values against which LSA findings can be "benchmarked" are based on surprisingly little data. Moreover, current LSA protocols potentially disadvantage speakers of nonmainstream English varieties, such as African American English (AAE), blurring the line between language difference and disorder. Method We provide a tutorial on the use of free software (Computerized Language Analysis [CLAN]) enabled by the ongoing National Institute on Deafness and Other Communication Disorders-funded "Child Language Assessment Project." CLAN harnesses the advanced computational power of the Child Language Data Exchange System archive (www.childes.talkbank.org), with an aim to develop and test fine-grained and potentially language variety-sensitive benchmarks for a range of LSA measures. Using retrospective analysis of data from AAE-speaking children, we demonstrate how CLAN LSA can facilitate dialect-fair assessment and therapy goal setting. Results Using data originally collected to norm the Diagnostic Evaluation of Language Variation, we suggest that Developmental Sentence Scoring does not appear to bias against children who speak AAE but does identify children who have language impairment (LI). Other LSA measure scores were depressed in the group of AAE-speaking children with LI but did not consistently differentiate individual children as LI. Furthermore, CLAN software permits rapid, in-depth analysis using Developmental Sentence Scoring and the Index of Productive Syntax that can identify potential intervention targets for children with developmental language disorder.

Narrative Language Markers of Arabic Language Development and Impairment

Purpose We examined indices of narrative microstructure as metrics of language development and impairment in Arabic-speaking children. We examined their age sensitivity, correlations with standardized measures, and ability to differentiate children with average language and language impairment. Method We collected story narratives from 177 children (54.2% boys) between 3.08 and 10.92 years old (M = 6.25, SD = 1.67) divided into six age bands. Each child also received standardized measures of spoken language (Receptive and Expressive Vocabulary, Sentence Imitation, and Pseudoword Repetition). Several narrative indices of microstructure were examined in each age band. Children were divided into (suspected) developmental language disorder and typical language groups using the standardized test scores and compared on the narrative indicators. Sensitivity and specificity of the narrative indicators that showed group differences were calculated. Results The measures that showed age sensitivity included subject omission error rate, number of object clitics, correct use of subject-verb agreement, and mean length of utterance in words. The developmental language disorder group scored higher on subject omission errors (Cohen's d = 0.55) and lower on correct use of subject-verb agreement (Cohen's d = 0.48) than the typical language group. The threshold for impaired performance with the highest combination of specificity and sensitivity was 35th percentile. Conclusions Several indices of narrative microstructure appear to be valid metrics for documenting language development in children acquiring Gulf Arabic. Subject omission errors and correct use of subject-verb agreement differentiate children with typical and atypical levels of language development.

Sampling Utterances and Grammatical Analysis Revised (SUGAR): Quantitative Values for Language Sample Analysis Measures in 7- to 11-Year-Old Children

Purpose The purpose of this study was to document whether mean length of utterance_SUGAR (MLU_S), total number of words (TNW), clauses per sentence (CPS), and/or words per sentence (WPS) demonstrated age-related changes in children with typically developing language, aged 7;0-10;11 (years;months). Method Participants were 132 typically developing children (aged 7;0-10;11), with a final sample size of 112 participants (57 boys and 55 girls). Fifty utterance conversational language samples were collected using a language sampling protocol. Four language sample analysis metrics (i.e., MLU_S, TNW, CPS, and WPS) were calculated from the samples. Results Results indicated statistically significant age-related increases in three (MLU_S, TNW, and WPS) of the four metrics. Conclusions MLU_S, TNW, CPS, and WPS may be used with other assessment data to document age-related language changes in children aged 7;0-10;11. When combined with previous data from younger (aged 3;0-7;11) children (Pavelko & Owens, 2017), the data suggest that these metrics offer a set of measures that can be used to assess children's conversational language skills from preschool through late elementary school.

Revisiting Reliability: Using Sampling Utterances and Grammatical Analysis Revised (SUGAR) to Compare 25- and 50-Utterance Language Samples

Purpose The goal of this study was to determine whether the results obtained from a 25-utterance conversational language sample were as reliable as those obtained from a 50-utterance sample. Method Robust conversational language samples from 220 children with typically developing language (106 boys, 114 girls) ranging in age from 3;2 to 7;10 (years;months) were collected. The language samples were randomly assigned to one of two conditions: a 25-utterance condition and a 50-utterance condition. Transcripts were examined for three metrics, including mean length of utterance_SUGAR, words per sentence, and clauses per sentence. Results Data were analyzed using two methods. A linear mixed-model analysis was used to assess absolute and relative reliability, and the Bland-Altman procedure was used to assess absolute reliability and clinical acceptability. Results of the mixed-model analysis indicated that mean length of utterance_SUGAR and words per sentence demonstrated relative reliability; however, none of the metrics demonstrated absolute reliability. In contrast, results of the Bland-Altman scatter plots indicated that all three metrics demonstrated absolute reliability because 94%-96% of participants' scores fell within the limits of agreement. Taken together, the results suggested that the statistically significant differences indicated by the mixed-model analysis were not clinically significant. Conclusion These results highlighted the importance of using different methods of analysis in studies of reliability. The findings indicated that reliable language sample results can be obtained from 25-utterance samples. Furthermore, by including practices already in use (e.g., collecting samples ≤ 50 utterances) and including only minimal changes to current practices, the methods used in this study are feasible for school-based clinicians, could be easily integrated into clinical practice, and could increase the use of evidence-based assessment practices in schools.

Use of Computerized Language Analysis to Assess Child Language

[Figure: see text].

Using Computer Programs for Language Sample Analysis

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

Mean Length of Utterance and Other Quantitative Measures of Spontaneous Speech in Russian-Speaking Children

Purpose This study investigated methodological and theoretical aspects of using mean length of utterance (MLU) and its alternatives in cross-linguistic research and, in particular, its applicability to Russian-a language with a rich system of grammatical and derivational morphemes. Method We collected audio recordings of spontaneous speech samples from 27 Russian-speaking children aged between 2;9 and 5;7 (years;months) over individual play sessions. For each participant, we transcribed the first 100 complete utterances and coded them for several types of utterance length measurements, including length in morphemes (grammatical and derivational), words, and syllables. At a sample level, we calculated the average number of produced unique grammatical forms, getting an alternative quantitative estimate of children's morphosyntactic abilities. Results A combination of Pearson correlation analysis and Bland-Altman difference plots established that MLU can be reliably used in Russian-speaking children aged around 3;0. The average number of unique grammatical forms remains a sensitive measurement of language capabilities even in older children aged over 3;6. Two quantitative measurements, MLU in syllables and morphemes, show good agreement, suggesting that these measurements can be used interchangeably across studies. Sample size analysis revealed that samples under 75 utterances do not provide sufficient reliability for estimating a child's MLU. Conclusions This article demonstrated that MLU can be used in young Russian-speaking children under 3;0-3;6. Also, we showed that the classical morpheme calculation approach can be substituted with counting syllables, which is more time efficient in the absence of automated parsers and is potentially more appropriate for some (e.g., polysynthetic) languages. Our proposed alternative to MLU-the average number of grammatical forms in a sample-appears to be a more sensitive measurement of language capabilities even in older children.

SUGAR (Sampling Utterances and Grammatical Analysis Revised): Breaking Tradition

Purpose

In this letter, the authors respond to Guo, Eisenberg, Ratner, and MacWhinney's (2018) letter to the editor, which expressed concerns regarding the clinical applicability of the Sampling Utterances and Grammatical Analysis Revised (SUGAR; Pavelko & Owens, 2017 ) language sample analysis (LSA) method.

Method

The authors respond to concerns regarding transcription and morpheme segmentation and language sample elicitation contexts. The results of two published research studies are described, which detail the impact of this new LSA method.

Results

Published research studies have documented that the SUGAR method of LSA can accurately distinguish between children developing language typically and children with language impairment and can also aid clinicians in developing intervention goals.

Conclusion

SUGAR is a clinically applicable method of LSA that clinicians can use as one part of a comprehensive evaluation to efficiently and accurately identify children with language impairment. Further, clinicians can complete subanalysis of a language sample to identify appropriate therapy targets.

Diagnostic Accuracy of the Sampling Utterances and Grammatical Analysis Revised (SUGAR) Measures for Identifying Children With Language Impairment

Purpose

The purpose of this study was twofold: (a) to determine the diagnostic accuracy of the four Sampling Utterances and Grammatical Analysis Revised (SUGAR) metrics, including total number of words, mean length of utterance SUGAR , words per sentence, and clauses per sentence in differentiating children with language impairment (LI) from those with typical language development, and (b) to compare the average time to collect, transcribe, and analyze 50-utterance language samples for children with LI to those with typical language development.

Method

Participants were 306 children (LI, 36; typical language development, 270) who ranged in age from 3;0 (years;months) to 7;11. Fifty-utterance conversational language samples were obtained using a conversational protocol. The four SUGAR metrics were calculated from the samples.

Results

Cut scores of −1 SD for mean length of utterance SUGAR and −1.25 cut score for clauses per sentence resulted in sensitivity of 97.22%, specificity of 82.96%, a positive likelihood ratio of 5.71, and a negative likelihood ratio of 0.03. On average, it took a total time of 20:20 min ( SD = 4:37, range: 13:11–30:25) to collect, transcribe, and analyze language samples for children with LI. Children with LI took significantly less time to produce 50 utterances, when compared to their typically developing peers. There were no significant differences in the time to transcribe and analyze language samples of children with LI compared to their typically developing peers.

Conclusions

The SUGAR metrics, in combination with other data sources (e.g., standardized testing, dynamic assessment, observation), can be used to identify preschool- and early elementary–aged children with LI. Furthermore, for children with LI, language sampling and analysis using the SUGAR method can be completed in approximately 20 min. The results of this study indicated the SUGAR measures can effectively and efficiently help in identifying LI.

Supplemental Material

https://doi.org/10.23641/asha.7728638

Is Putting SUGAR (Sampling Utterances of Grammatical Analysis Revised) Into Language Sample Analysis a Good Thing? A Response to Pavelko and Owens (2017)

Purpose

In this letter, the authors respond to Pavelko and Owens' (2017) newly advanced set of procedures for language sample analysis: Sampling Utterances and Grammatical Analysis Revised (SUGAR).

Method

The authors contrast some of the new guidelines for transcription, morpheme segmentation, and language sample elicitation in SUGAR with traditional conventions for language sample analysis (LSA). They address the potential impact of the new guidelines on some of the target measures in SUGAR-mean length of utterances in morphemes (MLUm), words per sentence (WPS), and clauses per sentence (CPS)-and provide their suggestions.

Results

Inclusion of partially intelligible utterances in SUGAR may over- or underestimate children's MLUm and reduce the reliability of computing WPS. Counting derivational morphemes and the component morphemes of catenatives (e.g., gonna) may result in overestimation of children's morphosyntactic skills.

Conclusion

Further data are needed to determine whether MLUm including derivational morphemes and the component morphemes of catenatives is a better measure of children's morphosyntactic skills than MLUm excluding those morphemes. Pending such data, the authors recommend maintaining traditional LSA conventions and measures. Furthermore, free, fast automated utilities already exist that reduce barriers for clinicians to conduct informative, in-depth LSA.