Search Results (172)

Child-directed speech (CDS) is characterized by a suite of exaggerated acoustic features, with elevated fundamental frequency (pitch) being a prominent and widely adopted component. While caregivers and educators frequently use high-pitch speech with young children, its perceptual preference among preschool-aged children, particularly in tonal languages like Mandarin, remains empirically unclear. This study aimed to investigate Mandarin-speaking preschoolers’ explicit preferences for manipulated pitch levels at the sentence frame while also examining the potential influence of lexical tone. Ninety-four children aged 3–6 years completed a binary forced-choice preference task. They listened to sentences systematically varying in three pitch levels (high, normal, low F₀) and five tone conditions (the four Mandarin lexical tones and a mixed-tone condition), with other acoustic parameters controlled. Results revealed that children showed no significant preference for high-pitch over normal-pitch speech. However, they exhibited a strong aversion to low-pitch speech. Furthermore, children’s pitch-level preferences were not modulated by the lexical tone of the sentences. These findings clarify that Mandarin-speaking preschoolers do not inherently prefer the high pitch typical of CDS over a normal speaking voice but are distinctly unfavorable toward low pitch. The study suggests that effective, listener-centered communication in early childhood settings may prioritize avoiding unusually low pitch rather than deliberately raising pitch, offering evidence-based guidance for pedagogical practice and adult–child interaction. Full article

Background/Objectives: Cross-linguistic studies show that the production of morphosyntactic elements (e.g., clitics) is problematic and often omitted in nonfluent agrammatic aphasia (NFA), with the degree of impairment varying across languages. Serbian, with its rich clitic system, provides a sensitive window into grammatical impairment. This study is the first to examine the production of proclitics and enclitics in Serbian speakers with aphasia and their relationship to short-term and working memory. Methods: Forty-six individuals with stroke-induced aphasia (25 NFA and 21 fluent aphasia [FA]) and 54 healthy controls completed an experimental Serbian clitic production test. Participants were prompted to produce clitic sentences (12 proclitics, such as prepositions or conjunctions, and 18 clitics, such as pronouns or auxiliary verbs) in response to various scenarios. Performances were correlated with sentence repetition and digit span (forward/backward). Results: Both aphasia groups produced significantly fewer clitics than controls (p < 0.001). Participants with NFA produced fewer overall clitics and showed no clitic type effects (p = 0.329), whereas participants with FA produced proclitics more accurately than enclitics (p = 0.028). Clitic production correlated with performance on sentence repetition and digit span tasks, but patterns differed by aphasia group. In NFA, both enclitics and proclitics were associated with sentence repetition and digit span (p < 0.05), whereas in FA, these measures were primarily associated with enclitic production (p < 0.05). Conclusions: Clitics production distinguishes nonfluent from fluent aphasia in Serbian and is differentially supported by working and verbal memory resources. The Serbian clitic production test reveals a selective proclitic advantage that is observed only in fluent aphasia, serving as a sensitive clinical marker in this population. Full article

This paper presents the first prototype of a speech-to-sign language translation system for Kazakh Sign Language (KRSL). The proposed pipeline integrates the NVIDIA FastConformer model for automatic speech recognition (ASR) in the Kazakh language and addresses the challenges of sign language translation in a low-resource setting. Unlike American or British Sign Languages, KRSL lacks publicly available datasets and established translation systems. The pipeline follows a multi-stage process: speech input is converted into text via ASR, segmented into phrases, matched with corresponding gestures, and visualized as sign language. System performance is evaluated using word error rate (WER) for ASR and accuracy metrics for speech-to-sign translation. This study also introduces the first KRSL dataset, consisting of 1200 manually recreated signs, including 95% static images and 5% dynamic gesture videos. To improve robustness under resource-constrained conditions, a Weighted Hybrid Similarity Score (WHSS)-based gesture matching method is proposed. Experimental results show that the FastConformer model achieves an average WER of 10.55%, with 7.8% for isolated words and 13.3% for full sentences. At the phrase level, the system achieves 92.1% accuracy for unigrams, 84.6% for bigrams, and 78.3% for trigrams. The complete pipeline reaches 85% accuracy for individual words and 70% for sentences, with an average latency of 310 ms. These results demonstrate the feasibility and effectiveness of the proposed system for supporting people with hearing and speech impairments in Kazakhstan. Full article

Down syndrome (DS) is associated with persistent language impairments that extend beyond early childhood, yet evidence from agglutinative languages remains limited. While morphosyntactic weaknesses have been well-documented in Indo-European languages, less is known about how such difficulties are manifested in Turkish, a language in which grammatical relations are primarily marked through morphology. In addition, short-term memory (STM) limitations, particularly in verbal domains, are characteristic of DS and may contribute to language outcomes. This study examined the interaction between morphosyntax and STM in Turkish-speaking children and adolescents with DS. A cross-sectional observational design was employed, including 12 monolingual Turkish-speaking participants with DS (aged 6;7–15;11) and 10 TD peers matched on nonverbal mental age. Participants completed standardized assessments of syntax and morphology, spontaneous language sampling, and STM tasks assessing verbal and visual memory. Children with DS performed significantly below controls on syntactic comprehension and production as well as morphological measures, with larger effects observed for syntax. Noun morphology was less accurate than verb morphology, likely reflecting increased morphophonological complexity. Regression analyses indicated that auditory digit span predicted sentence comprehension, whereas nonword repetition predicted morphological production indexed by mean length of utterance in morphemes. Substantial inter-individual variability was observed within the DS group. These findings suggest that morphosyntactic outcomes in Turkish-speaking children with DS are closely linked to verbal STM capacities and vary considerably across individuals, underscoring the importance of integrated assessment and individualized intervention planning. Future research with larger samples is warranted to confirm and extend these preliminary findings. Findings should be interpreted cautiously due to the limited sample size and are presented as preliminary descriptive evidence. This study provides initial data on Turkish-speaking individuals with Down syndrome. Full article

This study examined the value of Sentence Repetition (SRep) tasks as an ecologically valid tool for assessing bilingual children’s morphosyntactic competence. Seventy Greek–Turkish bilinguals and Greek monolinguals (aged 8–12) completed tasks assessing expressive vocabulary, morphological awareness, and SRep. Monolinguals significantly outperformed bilinguals across all tasks, with near-ceiling scores in grammaticality in SRep tasks reflecting earlier acquisition of core Greek structures. In contrast, bilinguals’ performance was significantly lower and varied across conditions: while scores were relatively higher on simple SVO, coordination, and wh-clauses, difficulties emerged in clitic left dislocation, complement clauses, and adverbial clauses—domains of greatest typological divergence between Greek and Turkish. Importantly, SRep performance on grammaticality did not vary with age, despite strong age effects on vocabulary and morphology, suggesting that SRep tasks indexes morphosyntactic knowledge rather than general maturational growth. Regression analyses showed that monolinguals’ SRep performance was best predicted by morphological awareness, whereas bilinguals relied more heavily on expressive vocabulary, reflecting their reduced exposure to Greek and reliance on lexical resources. These findings confirm the fairness and sensitivity of SRep for bilingual assessment, while highlighting the interplay of typological differences and input in shaping bilingual children’s morphosyntactic abilities. Full article

The main challenge of resource-poor languages—namely, the lack of sufficiently large and linguistically informed datasets for training neural models—is addressed in this paper by developing a dataset generation technology based on a Complete Set of Endings (CSE) morphological model for Turkic languages. Building on this technology, we propose a CSE-Guided Framework for morphology-aware statistical tokenization and neural model segmentation, with Kazakh as a case study. Applying the proposed CSE-guided approach to adapt well-known tokenizers for Kazakh leads to measurable reductions in neural model training time (up to approximately 33%) in our experimental setting, primarily due to shorter tokenized sentence lengths. In addition, we extend the SOTA FEMSeg-CRF architecture by incorporating Kazakh vowel–consonant harmony rules at the embedding generation stage. Within the proposed framework, training on a corpus of CSE-generated wordforms results in the FEMSeg_kaz_v2 model, which is evaluated using intrinsic segmentation metrics. Training on a CSE-segmented sentence corpus yields FEMSeg_kaz_v3, which is further assessed using intrinsic, extrinsic, and external evaluation on a manually prepared gold-standard dataset. The paper presents a CSE-guided framework for morphology-aware tokenization and segmentation for Turkic languages, supported by corpus construction, model extensions, and multi-level evaluation. The proposed CSE-Guided Framework can potentially be adapted for other Turkic languages. Full article

Word segmentation is a fundamental component of lexical processing, and Chinese reading—lacking inter-word spacing—requires readers to identify word boundaries based on prior experience. Previous studies have shown that contextual predictability facilitates lexical identification in Chinese reading; however, its influence on word segmentation remains unclear. This study used eye-tracking to examine the relationship between contextual predictability and readers’ segmentation preferences during Chinese sentence reading. Overlapping ambiguous three-character strings (e.g., 花生长) were used as the region of interest (ROI), and a 2 (segmentation type: AB-C (e.g., 花生/长) vs. A-BC (e.g., 花/生长)) × 2 (contextual predictability: high vs. low) within-subjects design was adopted. A total of 76 native Chinese speakers completed the task. The results showed that contextual predictability had a significant effect on skipping probability: Highly predictable target character strings were skipped more often than low-predictability words. However, contextual predictability did not influence any eye-movement measure. In contrast, segmentation type produced consistent effects across all measures, with shorter reading times for AB-C than for A-BC, indicating a stable preference for two-character segmentation. More importantly, no interaction emerged between contextual predictability and segmentation type, and Bayesian model comparison further supported this conclusion. These findings suggest that Chinese reading involves a robust preference for AB-C segmentation and that contextual predictability and word segmentation operate as independent processes, with predictability exerting minimal influence on word segmentation during reading. This result supports the Chinese Reading Model (CRM). Full article

Clinical rotations are an integral part of the veterinary curriculum. Their effect on knowledge, skills and self-perception, however, has been poorly investigated. The aim of this study was to evaluate the effect of a structured small animal clinical rotation on veterinary students in these three areas. Participating students were asked to complete an online questionnaire with questions assessing knowledge, skills and self-perception before and after their clinical rotation. A total of 61 students completed the questionnaire before and 43 after the clinical rotation, leading to 41 pre-post matches for self-assessment of skills and self-perception and 39 pairs for knowledge-based questions. The percentage of correctly answered knowledge-based questions increased statistically significantly but only by one correct answer on average. Participants rated their skills in performing specific tasks significantly higher after the clinical rotation compared to before. All participants assessed themselves as competent at history taking, performing a general examination and endotracheal intubation after the clinical rotation. However, 30–40% of participants disagreed at least partly with the sentence that they can perform neurological and ophthalmological examinations as well as interpret blood results on their own after the clinical rotation. Participants rated themselves significantly higher regarding their self-perception in the role of small animal practitioner after the clinical rotation than at the start of the rotation. The study indicated that the clinical rotation improved students’ self-assessment of their skills and attitude but did not lead to a significant improvement in knowledge. Full article

This study examines whether Drawmetrics (DM), an expressive–semantic personality system, can be linked with the Five-Factor Model (Big Five) through an embedding-based mapping approach and network psychometric methods. A total of 185 participants completed both the DM assessment and the IPIP-NEO 120 Big Five inventory. DM term outputs were embedded using a miniLM sentence-transformer and aggregated into 30 facet composites, with six composites per domain. Big Five facet composites were extracted from standardized reports and harmonized to canonical facet names. Analyses focused on the overlap sample (N = 148) with valid scores on both instruments. DM composites demonstrated strong internal structure and high stability indices. Substantial semantic-space alignment was observed between DM term language and Big Five facet language, supporting interpretable linking. However, person-level correlations between DM and Big Five domains were modest (mean |r| ≈ 0.07; Spearman similar), with the largest facet-level association at |r| ≈ 0.26. DM appears to represent a coherent expressive–semantic trait space that is related to, but not isomorphic with, Big Five traits. These findings support a linking rather than equivalence interpretation and highlight the need for future research on scaling, reliability, range restriction, and criterion validation. Full article

Extracting and organizing knowledge from the agricultural crop disease research literature are challenging tasks because of the heterogeneous terminologies, complicated symptom descriptions, and unstructured nature of scientific documents. In this study, we developed a multi-stage natural language processing (NLP) pipeline to automate knowledge extraction, organization, and integration from the agricultural research literature into a domain-consistent crop disease knowledge graph. The model combines transformer-based sentence embeddings with variational deep clustering to extract topics, which are further refined via facet-aware relevance scoring for sentence selection to be included in the summary. Lexicon-guided named entity recognition helps in the precise identification and normalization of terms for crops, diseases, symptoms, etc. Relation extraction based on a combination of lexical, semantic, and contextual features leads to the meaningful generation of triplets for the knowledge graph. The experimental results show that the method yielded consistently good results at each stage of the knowledge extraction process. Among the combinations of embedding and deep clustering methods, SciBERT + VaDE achieved the best clustering results. The extraction of representative sentences for disease symptoms, control/treatment, and prevention obtained high F1-scores of around 0.8. The resulting knowledge graph has high node coverage and high relation completeness, as well as high precision and recall in triplet generation. The multi-stage NLP pipeline effectively converts unstructured agricultural research texts into a coherent and semantically rich knowledge graph, providing a basis for further research in crop disease analysis, knowledge retrieval, and data-driven decision support in agricultural informatics. Full article

In the energy industry, like industrial monitoring scenarios, using generative AI for video captioning technology is crucial in event understanding and safety analysis. Current approaches typically rely on a single language model to decode visual semantics from video frames. Lightweight pre-trained generative models often produce overly generic captions that omit domain-specific details like energy equipment states or procedural steps. Conversely, multimodal large generative AI models can capture fine-grained visual cues but are prone to distraction from complex backgrounds, resulting in hallucinated descriptions that reduce reliability in high-risk energy workflows. To bridge this gap, we propose a collaborative video captioning framework, EnerSafe-Cap (Energy-Safe Video Captioning), which introduces domain-aware prompt engineering to integrate the efficient summarization of lightweight models with the fine-grained analytical capability of large models, enabling multi-level semantic understanding, thereby improving the accuracy and completeness of video content expression. Furthermore, to fully exploit the strengths of both small and large models, we design a dual-path heterogeneous sampling module. The large model receives key frames selected according to inter-frame motion dynamics, while the lightweight model processes densely sampled frames at fixed intervals, thereby capturing complementary spatiotemporal cues global event semantics from salient moments and fine-grained procedural continuity from uniform sampling. Experimental results on commonly used benchmark datasets show that our model outperforms baseline models. Specifically, on the VATEX dataset, our model surpasses the lightweight pre-trained language model SwinBERT by 19.49 in the SentenceBERT metric, and outperforms the multimodal large language model Qwen2-vl-2b by 8.27, validating the effectiveness of the method. Full article

Using the Action–Sentence Compatibility Effect (ACE) paradigm, this study investigated whether types of grammatical aspects and L2 proficiency influence embodied simulation during L2 sentence comprehension of English among Chinese learners. Participants judged the semantic plausibility of sentences in progressive or perfective aspect by performing directional actions (toward or away the body) that were either compatible or incompatible with the action direction described. Analysis of the reaction times (RTs) revealed a significant main effect of proficiency, with low-proficiency learners responding more slowly overall. Crucially, we observed a significant three-way interaction between aspect, action–sentence consistency, and proficiency. Simple effects analyses revealed a qualitative reversal: advanced learners exhibited a significant ACE only for sentences in the progressive aspect, indicating grammatically guided simulation sensitive to ongoing actions, whereas we found no ACE for perfective sentences, consistent with their focus on event completion rather than on-going action processes. In contrast, low-proficiency learners showed a significant ACE for the perfective aspect, suggesting a reliance on lexically triggered simulation, while they showed no simulation effect for the progressive aspect due to shallow morphosyntactic processing and L1 transfer. These findings support a proficiency-dependent dual-pathway model of L2 embodiment: advanced learners engage in direct mapping from grammar to simulation, whereas low-proficiency learners rely on an indirect, lexically mediated route. In summary, our findings demonstrate that the embodiment of grammatical meaning in L2 acquisition is not automatic, but is developmentally modulated, evolving from a lexically dependent to grammar-dependent simulation as proficiency increases. Furthermore, these results call for future research to explore the pedagogical applications of grammar-focused embodied instruction and to examine this dual-pathway model across other linguistic structures and L2 populations. Full article

Bug reports in open-source projects are often incomplete or low in quality, which reduces maintenance efficiency. To address this issue, we propose AgentReport, a multi-agent pipeline based on large language models (LLMs). AgentReport integrates QLoRA-4bit lightweight fine-tuning, CTQRS (Completeness, Traceability, Quantifiability, Reproducibility, Specificity) structured prompting, Chain-of-Thought reasoning, and one-shot exemplar within seven modules: Data, Prompt, Fine-tuning, Generation, Evaluation, Reporting, and Controller. Using 3966 summary–report pairs from Bugzilla, AgentReport achieved 80.5% in CTQRS, 84.6% in ROUGE-1 Recall, 56.8% in ROUGE-1 F1, and 86.4% in Sentence-BERT (SBERT). Compared with the baseline (77.0% CTQRS, 61.0% ROUGE-1 Recall, 85.0% SBERT), AgentReport improved CTQRS by 3.5 percentage points, Recall by 23.6 points, and SBERT by 1.4 points. The inclusion of F1 complemented Recall-only evaluation, offering a balanced framework that covers structural completeness (CTQRS), lexical coverage and precision (ROUGE-1 Recall/F1), and semantic consistency (SBERT). This modular design enables consistent experimentation and flexible scaling, providing practical evidence that multi-agent LLM pipelines can generate higher-quality bug reports for software maintenance. Full article

Some generative linguists report that in formal settings, learners of English as a foreign language often strive to acquire morphemes such as the third-person singular –s and produce utterances such as *he play. This study reviews generative linguistics, psychology, neuroscience, and biolinguistics, examining how speech and other forms of action involve hierarchically organised groups (chunks) of words or acts that are invariably produced in linear order. Chunks contribute to brain efficiency, facilitating acquisition and enabling brain automaticity. A study was conducted to improve the accuracy rates of sentence segments featuring the third-person singular –s (e.g., “he VERB+s”) by orally rehearsing chunk-based sentences (e.g., [He plays] [a lot]). Sixty-four children from three Spanish schools, learning English as a foreign language and aged 8–11, participated in this study. The participants, divided into a control group and two experimental groups, completed an oral sentence transformation task following a pre-test–post-test design. The Wilcoxon test showed statistically significant results for the experimental groups after the administration of oral chunk-based training. Quartiles and deciles demonstrated improvement in these groups. The findings suggest that oral chunk-based training could foster chunk and morpheme acquisition. This pedagogy might enhance brain efficiency in learning and promote automatic speech. Full article

Contemporary issues of particular concern include the current state of the teaching profession, the lack of professional and social respect for teachers, the need for salary increases, the need to reduce the burden on teachers, and performance evaluation. In addition, the low number of young people entering the teaching profession and the low proportion of graduates choosing this career path are of fundamental concern. The present research seeks to explore the perceptions of first-year student teachers towards the profession of teaching through a case study approach. A metaphor method involving sentence completion was used to explore students’ conceptions of the ‘teacher image’. The students’ metaphors were analyzed to determine the prevalence of teacher-centeredness or learner-centeredness, knowledge transfer or knowledge acquisition, and cognition or emotion. The students’ essays on the question “Why do I want to be a lower primary teacher?” were analyzed to identify career motivating factors, positive attributes that enhance career motivation, and future aspirations. The main findings of the research are that trainees are mainly inspired by their primary school teachers, they are aware of their strengths to become good teachers, and they envision a career in teaching. The motivation and commitment of our first-year students to their careers is an excellent starting point, which should be built on in both theoretical and practical courses during university education. They should be enriched with real-life experiences, encouraged and supported within their practice, in order to increase the number of young people who choose a teaching career after graduating from university. Full article