Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of speech, language, and hearing research : JSLHR
Language-Common and Language-Specific Error Patterns in Children With Speech Sound Disorders: A Systematic Review
OBJECTIVES: This systematic review synthesized cross-linguistic evidence on speech error patterns in children with speech sound disorders (SSDs), focusing on percent consonants correct (PCC), SODA (substitution, omission, distortion, and addition) error profiles, and phonological processes, as well as factors contributing to variations in error patterns. METHOD: Comprehensive searches were conducted in PubMed, CINAHL Ultimate, Scopus, Web of Science, and Linguistics and Language Behavior Abstracts in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eligible studies included empirical research on children with phonological, articulation, or functional SSDs that reported quantitative error analyses. Twenty-eight studies met the inclusion criteria. Methodological quality, evaluated using a 16-item framework, ranged from 50.0% to 100.0% (M = 82.7%). RESULTS: The 28 studies spanned nine languages (English, German, Spanish, Portuguese, Italian, French, Turkish, Korean, and Chinese), including three dialects each of Spanish and Chinese and two dialects of Portuguese. Reported PCC values (typically 65.0%-85.0%) indicated predominantly mild-moderate to moderate impairment. Substitution errors were the most prevalent across languages, followed by omissions and distortions. Both language-common and language-specific phonological processes were documented. DISCUSSION: The findings indicate that PCC is generally comparable across languages, whereas SODA error profiles and phonological processes reflect both cross-linguistic similarities and language-specific characteristics. Phonological processes, in particular, exhibit greater cross-linguistic variation. Collectively, these results emphasize the need for culturally responsive diagnostic practices and highlight the importance of developing cross-linguistic frameworks to guide the assessment of SSDs. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.32649549.
10 July 2026
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Statistical Analysis of Human and AI Machine Translation Accuracy, Style, and Context in Academic English
AI-driven Machine Translation (MT) technology has raised doubts about whether it can replace Human Translation (HT), particularly in the academic English field. This research contrasts AI-based MT and HT of academic English on accuracy, stylistic quality, errors, contextual appropriateness, and overall suitability. A quantitative comparative design was employed for the translation of eight Chinese texts with various genres. Using purposive sampling, 4 certified human translators were selected to produce the HT, and also used AI-based MT systems such as DeepL, ChatGPT, and Google Translate to translate the text. 69 experts evaluated the translated versions, and this data was examined using SPSS software. HT produced better performance than MT through mean accuracy, stylistic quality, contextual appropriateness, and overall suitability (Human: 4.76, AI: 3.73), revealed statistically significant differences (p < 0.001), and effect sizes (Cohen's d = 1.53 to 2.81). Compared to HT, the MT showed higher grammatical and semantic errors (Cohen's d = 2.81). The fluency, tone, and register of stylistic quality were evaluated at higher levels for HT (p < 0.001). Among AI-MT tools, DeepL shows higher performance in translation (M = 4.16, SD = 0.37). The genre-wide comparison of MT and professionally certified HT, and the comparison among multiple AI tools, towards various translation dimensions, provides insights into the differences in translating quality. The research indicates a continued reliance on human professionals for important scholarly translations while encouraging continued development in AI translation software for better quality.
22 June 2026
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