Abstract Background Speech intelligibility is the degree to which the speaker’s intended message is recovered by the listener. There are several measures available for speech intelligibility assessment. Multiple studies have examined speech intelligibility assessments across different languages and populations. However, there remains a lack of specific speech intelligibility tests for Indian languages. The present study aims at the development of test stimuli and content validation of language-free word stimuli to assess speech intelligibility. Method The study was conducted in four phases which includes the following: enumerating consonants available in the Dravidian language family and content validation; development and content validation of language-free word stimuli to assess speech intelligibility; content validation of audio-recorded language-free word stimuli, and development and content validation of rating scale and administration of test stimuli in neurotypical individuals . Results Initially, a total of 17,756 words using combination generation in Excel, which was narrowed down to 4000 trisyllabic words with the vowel /a/. Further refinement resulted in 348 trisyllabic words based on the frequency of phonemes in Dravidian languages, considering all consonant positions. Content validation was conducted using the Delphi technique. The final selection of trisyllabic words was based on the content validation ratio (CVR) and content validation index (CVI) scores, resulting in 119 relevant words. The recording of those words was carried out by four speakers (two male and two female). Those recordings were validated by two speech-language pathologists using a ranking system. Based on the ranking, the investigator selected a male speaker who had the best clarity as a source of recording. The investigator administered the prerecorded trisyllabic word stimulus to the participants (30 neurotypical individuals; the age range of 19–65 years) bilaterally. The investigator asked the participant to repeat those words, whatever they heard through the headphones, and those responses were transcribed and analysed by the primary investigator. Out of a total of 119 words, 45 were eliminated from the list because they were (neurotypical individuals) produced incorrectly. The final word list contains 74 trisyllabic words. Conclusion The language-free stimuli can be used for the speech intelligibility assessment of native Dravidian language speakers with dysarthria, neurological conditions like Parkinson’s disease, bulbar palsy, post-glossectomy patients, etc. Future studies can focus on the clinical validation of the stimuli among disorder populations such as dysarthria.

There is a pressing need for well-defined phonological assessment protocols and the development of valid, reliable, and clinician-friendly tools tailored to the unique linguistic contexts of each country. This study reports the initial phase of establishing normative data on phonological development in Persian-speaking children aged 24–84 months. The objective was to develop a robust set of single words varying in length and syllabic structure for use in speech sound assessment. Using a content analysis approach, 108 words were selected, ranging from one to six syllables (35 monosyllabic, 26 disyllabic, and 47 polysyllabic). Word selection was guided by psycholinguistic principles, including phoneme frequency, phonotactic diversity, syllabic complexity, and lexical familiarity. Criteria ensured cultural appropriateness, absence of repeated phonemes, diverse syllable structures, and inclusion of all consonants across positions. The stimuli are designed to probe underlying speech motor planning and phonological encoding processes in children. These words were organized into 18 semantically related image sets, each containing six words. Monosyllabic and disyllabic items included at least two Persian consonants in both initial and final syllable positions, and each consonant was represented by at least one polysyllabic word. Eight clinically-experienced adults evaluated name-image agreement, while 10 children aged 30–54 months demonstrated over 50% spontaneous naming and over 70% familiarity with the words. The final stimulus set includes a substantial number of polysyllabic words and appears suitable for analyzing phonetic and phonemic inventories, and phonological error patterns in Persian-speaking children.

Monolingual children tend to assume that a word labels only one object, and this mutual exclusivity supports referent selection and retention of novel words. Bilingual children accept two labels for an object (lexical overlap) for referent selection more than monolingual children, but in these previous studies, information about speakers' language backgrounds was minimal. We investigated monolingual and bilingual 4-year-old children's ability to apply mutual exclusivity and lexical overlap flexibly when objects were labelled either by one or two speakers with the same or different language backgrounds. We tested referent selection and retention of word-object mappings. Both language groups performed similarly for mutual exclusivity, were more likely to accept lexical overlap in the two-language than one-language condition, and performance was similar for referent selection and later retention. Monolingual and bilingual children can adapt their word-learning strategies to cope with the demands of different linguistic contexts.

Understanding the challenges inherent in Chinese–Spanish sight translation for undergraduate students is essential for enhancing their interpretation ability and accuracy. However, sight translation errors have rarely been studied, especially for Chinese–Spanish language pair. This study builds a corpus of Chinese university students’ Chinese–Spanish sight translation errors, which consists of 294 audio assignments and 2,923 error instances. The corpus of this study has three parameters: error levels, namely, the lexical, syntactic and grammatical; error manifestations that include substitution, addition, and omission; and source text analysis units, which are interpreting topics and sentences. Based on a combination of theories including the error analysis, Contrastive Analysis Hypothesis, the schema theory and Gile’s Effort Model, error analysis was conducted on the corpus to identify prevalent error types, analyze error distribution patterns, and determine error causes. This study employed a combination of quantitative and qualitative analysis. Frequency analysis, chi-square test and error rate were calculated to determine the prevalent error types and error distribution patterns. A qualitative analysis was also realized to determine the causes of errors. Results indicate that substitution was the most frequent error manifestation, whereas addition and omission were much less frequent. Regarding error levels, at the lexical level, sentences with difficult lexical expressions, like four-character words, abstract words, and poetic phrases, tend to concentrate more errors. Substitution of word selection and terminology, and omission of words were prevalent. At the syntactic level, sentence structure and omission of syntactic elements occurred most frequently, particularly in sentences with considerable length or complex structures. At the grammatical level, errors predominantly occur in areas where Chinese and Spanish have strong linguistic differences, such as agreement in gender and number, verb tense and conjugation, prepositions, and articles. The error causes constitute a complex mechanism that includes linguistic differences, negative translation of mother tongue, lack of domain knowledge and cultural understanding, cognitive load, and other factors. This study offers insights into error patterns and their causes in Chinese–Spanish sight translation, and provides a foundation for future studies on various areas in interpreting error analysis and interpreter training.

Generative linguistic steganography aims to produce texts that remain both perceptually and statistically imperceptible. The existing embedding algorithms often suffer from imbalanced candidate selection, where high-probability words are overlooked and low-probability words dominate, leading to reduced coherence and fluency. We introduce a character-based diffusion embedding algorithm (CDEA) that uniquely leverages character-level statistics and a power-law-inspired grouping strategy to better balance candidate word selection. Unlike prior methods, the proposed CDEA explicitly prioritizes high-probability candidates, thereby improving both semantic consistency and text naturalness. When combined with XLNet, it effectively generates longer sensitive sequences while preserving quality. The experimental results showed that CDEA not only produces steganographic texts with higher imperceptibility and fluency but also achieves stronger resistance to steganalysis compared with the existing approaches. Future work will be to enhance statistical imperceptibility, integrate CDEA with larger language models such as GPT-5, and extend applications to cross-lingual, multimodal, and practical IoT or blockchain communication scenarios.

The increasing number of tourism-related websites has captured the interest of both linguists and communication professionals, motivating them to examine the persuasive functions embedded in the digital content designed to appeal to a global audience. These websites, operating within a corporate framework, have undergone a noticeable transformation in how they present promotional materials, increasingly relying on strategic, persuasive communication to influence potential tourists. In this context, the official tourism website serves not merely as an informational platform but as a tool of digital persuasion. The article focuses on exploring various persuasive approaches, strategies, and rhetorical tactics employed in online tourism discourse, using the “Visit Almaty” website. Through this analysis, the research aims to reveal how tourism promotion has evolved in its use of language and design to construct a compelling narrative that aligns with both corporate identity and local cultural values. Moreover, the study investigates the structure and layout of the website, analyzing how design elements and content organization contribute to effective information dissemination and brand portrayal. It highlights how the selection of words, images, and multimedia elements does more than promote a travel destination—they serve as cultural signifiers that encapsulate the city’s unique social, historical, and cultural character. As such, these components provide rich ground for linguistic and communicative analysis. The results of the research show the importance of using persuasive function in tourism Internet discourse to attract the readers and visitors. The article’s theoretical significance lies in its contribution to digital communication research, especially in tourism discourse, by revealing how language and design shape persuasive narratives. Its practical value is in guiding tourism marketing and web design through effective strategies that attract and engage audiences.

Fake news generation and propagation occurs in large volumes, at high speed, in diverse formats, while also being short-lived to evade detection and counteraction. Despite its role as an enabler, Artificial Intelligence (AI) has been effective at fake news detection and prediction through diverse techniques of both supervised and unsupervised machine learning. In this article, we propose a novel Artificial Intelligence (AI) approach that addresses the underexplored attribution of information asymmetry in fake news detection. This approach demonstrates how fine-tuned language models and emotion embeddings can be used to detect information asymmetry in intent, emotional framing, and linguistic complexity between content creators and content consumers. The intensity and temperature of emotion, selection of words, and the structure and relationship between words contribute to detecting this asymmetry. An empirical evaluation conducted on five benchmark datasets demonstrates the generalizability and real-time detection capabilities of the proposed AI approach.

This study explores the semantic fields of love and loss in Lord Huron’s song "The Night We Met," using a lexical semantics framework. Song lyrics, known for their emotional depth, serve as a rich source for analyzing affective meaning, particularly through word selection and repetition. The primary research question addresses how semantic fields are constructed within minimalist lyrical compositions to convey complex emotional states. In this analysis, a qualitative descriptive method is applied to examine the song's lexical items and group them into thematic semantic fields. The study finds that the song uses a concise set of lexemes connected to themes of memory, temporal disorientation, grief, and love. These thematic fields intertwine, interacting to depict a narrative that encapsulates unresolved emotional pain. Love and loss are not presented in isolation but are embedded in a broader emotional context, where words evoke both the sweetness of past affection and the sorrow of its absence. The overlap of these semantic fields reflects the internal conflict experienced by the narrator, torn between cherished memories and the painful realization of loss. The study concludes that despite the song’s brevity, the lyrics effectively evoke profound emotional responses through a meticulous choice of words and thematic consistency. The semantic fields constructed within the song illustrate how even minimal language can convey intense emotional content, providing valuable linguistic insight into the function of lyricism in popular music. The research demonstrates the ability of short, poignant lyrics to engage listeners emotionally, revealing how lyricists use limited language to encapsulate complex feelings of love and loss.

Metadiscourse is a significant linguistic tool for engaging the audience as it directs readers through the text. Throughout the pandemic, news about Coronavirus Disease 2019 (COVID-19) became a vital public information source. Nevertheless, reference books, manuals, and websites do not provide clear guidelines for composing news about COVID-19. Because of the variations in word selection, expressions and sentences, readers experience misinterpretations. This research seeks to examine how readers react to news articles that include metadiscourse compared to those that do not. To obtain insights from this research, 30 students from a private school in Malaysia took part in the study. They were provided with news containing both metadiscourse and non-metadiscourse and writing prompts. The results showed that most participants favoured reading the news with textual metadiscourse, emphasising writers' authority due to its clarity. Moreover, the results indicated that most participants preferred the news featuring interpersonal metadiscourse that stressed readers' engagement as it was more captivating. The study examined the participants' responses to readers with guidelines to ensure reader engagement is highlighted in their manuscripts. The results highlighted the significance of metadiscourse in writing, indicating that texts created with and without metadiscourse may serve as valuable teaching materials for writing instructors in educational settings.

This research analyzes lexical cohesion in Prabowo Subianto's presidential speech at Al-Azhar University Cairo in December 2024. By using descriptive qualitative method, this research aims to identify and analyze the types of lexical cohesion applied, including repetition, synonymy, antonymy, hyponymy, collocation, and equivalence. The data of this research comes from the transcription of speeches uploaded through the YouTube channel Kompas.com. The analysis shows that repetition is the most dominant type of lexical cohesion used. Out of 78 data, repetition is found as much as 50 data, synonymy 3 data, antonymy 3 data, hyponymy 1 data, collocation 13 data, and equivalence 8 data. The dominant use of repetition reflects President Prabowo's efforts to strengthen his message and ensure the listeners' deep understanding. The key points in the repetition reflect the fundamental principles and identity of the Indonesian nation in the context of international relations, including mutual respect, rejection of hostility, and the essence of inclusive cooperation regardless of differences in order to realize unity and prosperity. This study concludes that lexical cohesion plays a significant role in building discourse cohesion and clarifying meaning in President Prabowo's speech. The careful selection of words based on semantic relationships contributes to the effective delivery of the message and the meaning of the speech.

The purpose of this study was to investigate the effect of an intervention package that included constant time delay (CTD) and sentence frames on correct word selection for sentences constructed using technology-aided instruction (TAI) during a story-based lesson (SBL) for participants ages 6 to 8 years who have intellectual and/or developmental disabilities (IDD). A single-case multiple-probe across participants design was used to evaluate the efficacy of the intervention. Probes were conducted during baseline, intervention, generalization, and maintenance phases to determine the effectiveness of the intervention on correct word selection for sentence construction. Results indicate a functional relation between the intervention package and correct word selection for sentence construction during a shared story. In addition, the Percent of Nonoverlapping Data (PND) indicates overall strong effects. Finally, students were able to demonstrate both generalization and maintenance of skills.

This study aimed to explore 1) Thai learners’ selections of single-word and multi-word units in English language production, and 2) the reasons for their selections of single-word and multi-word units. The participants in this study comprised 202 undergraduate students. Three research instruments were used in this study: a comprehension test, a word selection test, and an avoidance and preference questionnaire. The findings showed that in total, the learners tended to favor multi-word units more than single-word units. The results also indicated that the learners preferred literal phrasal verbs and compounds over their single-word equivalents. However, single-word units were preferred over figurative phrasal verbs and idioms by the learners. Based on the t-test results, overall, there were no statistically significant differences in the learners’ selection of single-word and multi-word units. Nevertheless, the learners’ responses to the avoidance and preference questionnaire reflected their preference for selecting both word types (single-word and multi-word units) more than their avoidance. This implies that preference may have a significant influence on their selection of word types, at least more so than avoidance. This may imply that preference have a higher effect on their selection of both word types than avoidance.

This study aims to explore the attitudes and acceptance levels of communication practitioners toward the use of artificial intelligence (AI) technologies in the production of news content, contributing to the development of media and technology strategies that ensure high-quality news services. The study is grounded in the Technology Acceptance Model (TAM) and was conducted with a sample of 200 communication professionals. Findings revealed a growing awareness and positive orientation among practitioners toward the integration of AI applications in journalistic work, with more than half of the sample regularly utilizing such tools—particularly in translation and editing—to enhance both speed and quality. Results also highlighted a strong recognition of the importance of digital skills and a high appreciation of AI’s benefits, such as data analysis, audience-based word selection, and profit generation. Based on these findings, the study recommends developing continuous training programs, providing advanced technological infrastructure, establishing regulatory policies that ensure ethical use, and strengthening media-academic collaboration to implement AI effectively in newsrooms. Keywords: Communication practitioners – Artificial intelligence – Technology acceptance – News content

AI-based paraphrasing tools for English writing have emerged due to technological advancements in natural language processing and AI. These tools help users reformulate texts while retaining their original meaning. Utilizing advanced algorithms, they analyze sentence structure and context, producing efficient paraphrases. These tools save time and effort, making them a popular solution for English paraphrasing. This research examines the capabilities of online paraphrasing tools, focusing on their ability to process input text, understand context, decipher word meaning, sentence structure, and relationships between words while identifying important phrases or keywords. The analysis reveals that various paraphrasing tools, including QuillBot, Ginger Writer Rephraser, Spinbot, paraphraser.io, EditPad, Rephrase, PrePostSEO, and SmallSEO, produce paraphrases similar to the original text but with some changes in sentence structure and word choice. These tools offer benefits such as efficiency, consistency, and plagiarism prevention but also allow users to customize wording for different audiences and communication requirements. However, they have limitations such as varying output quality, difficulty in capturing complex nuances, language limitations, and errors in word selection. The quality of paraphrasing results depends on the training data used, and the algorithm may struggle with processing lengthy or intricate sentences. Despite their effectiveness, paraphrasing tools can still leave users vulnerable to unintentional plagiarism if the output is inadequate or inaccurate. Users must exercise caution and be prepared to review and refine their output to ensure reliability and integrity. AI-based paraphrasing tools need further development to improve their quality and reliability. Regular grammar checks are necessary to ensure accuracy and coherence, but AI algorithms may overlook some, leading to lingering inaccuracies. Combining AI tools with manual review ensures high-quality paraphrased content that meets specific needs and objectives.

AI-based paraphrasing tools for English writing have emerged due to technological advancements in natural language processing and AI. These tools help users reformulate texts while retaining their original meaning. Utilizing advanced algorithms, they analyze sentence structure and context, producing efficient paraphrases. These tools save time and effort, making them a popular solution for English paraphrasing. This research examines the capabilities of online paraphrasing tools, focusing on their ability to process input text, understand context, decipher word meaning, sentence structure, and relationships between words while identifying important phrases or keywords. The analysis reveals that various paraphrasing tools, including QuillBot, Ginger Writer Rephraser, Spinbot, paraphraser.io, EditPad, Rephrase, PrePostSEO, and SmallSEO, produce paraphrases similar to the original text but with some changes in sentence structure and word choice. These tools offer benefits such as efficiency, consistency, and plagiarism prevention but also allow users to customize wording for different audiences and communication requirements. However, they have limitations such as varying output quality, difficulty in capturing complex nuances, language limitations, and errors in word selection. The quality of paraphrasing results depends on the training data used, and the algorithm may struggle with processing lengthy or intricate sentences. Despite their effectiveness, paraphrasing tools can still leave users vulnerable to unintentional plagiarism if the output is inadequate or inaccurate. Users must exercise caution and be prepared to review and refine their output to ensure reliability and integrity. AI-based paraphrasing tools need further development to improve their quality and reliability. Regular grammar checks are necessary to ensure accuracy and coherence, but AI algorithms may overlook some, leading to lingering inaccuracies. Combining AI tools with manual review ensures high-quality paraphrased content that meets specific needs and objectives.

The discourse surrounding advertisement language style is noteworthy in contemporary communication processes. This survey explores several facets, elucidating key dimensions within this domain. The survey underscores the significance of determining the dominant theme and motivational elements intended for brand advertising. Notably, family-related themes emerge as the preference among most respondents, particularly in the context of construction material or confectionery advertisements. Findings indicate that protracted texts, laden with information, lack memorability. The appropriateness of such comprehensive textual content in advertisements is questioned, suggesting a propensity among audiences for concise and impactful messaging. The study discerns the establishment of a distinct influence milieu within advertisements through the deployment of spoken units. Noteworthy is the cultivation of word collocations, which evolved into an indispensable marketing attribute. Within this paradigm, food brand slogans such as «Do Snickers,» «Coca-Cola Forever,» and «Chosen grain: The secret of the taste» stand out, showcasing the potency of language in fostering memorability. The survey underscores the criticality of word selection and collocation in advertising. Remarkably, the strategic pairing of words and the employment of specialized vocabulary to enhance persuasive appeal exhibit a profound impact. Noteworthy instances include food brand slogans, which are highly memorable linguistic constructs. In synthesizing survey statistics, a discernible societal inclination toward family-related themes in construction material and confectionery advertisements is evident. Furthermore, the study posits that the social-psychological proclivity of the populace is distinctly polarized toward the food industry, a phenomenon satisfactorily addressed by current advertising practices. This research contributes substantively to our understanding of the nuanced interplay between advertisement language, societal preferences, and cognitive responses, thereby advancing the scholarly discourse in this domain. Հոդվածում ընդհանուր կողմերով ներկայացվում են արդի հայերենի գովազդային տեքստերում բառընտրության և բառազուգորդումների յուրահատկությունները: Խոսքի սեղմության և մատչելիության արժանիքները պահպանելու համար տեքստերում հաճախ գործածվում են համագործածական և խոսակցական բառեր, պատրաստի լեզվակաղապարներ ևայլն: Բացի այդ` բառընտրության գործում մեծ դերակատարություն ունի թեմայի և թիրախավորված լսարանի հստակ որոշարկումը, քանի որ դրանք են նաև պայմանավորում, թե գովազդն ինչպիսի լեզվական հնարքներով պետք է ներկայացվի: Արդի գովազդներում ունենք սեղմ, հետաքրքրող և ներազդող տեքստեր, որոնք կազմված են ոլորտի «օրենքներով »: Բայց միաժամանակ կան այնպիսիք, որոնք սեղմ ժամանակի մեջ փորձում են մեծ ու սպառիչ տեղեկատվություն տալ` առանց նկատի առնելու բառընտրության և խոսքը լսարանին հասցեագրելու խնդիրները: Այս դեպքերում հոդվածում առաջարկվում են երկարաբան տեքստերի շտկման նմուշներ, որոնք կարող են օգտակար լինել հետագայում այդօրինակ սխալներից խուսափելու համար: Առանձնակի ուշադրություն է դարձվում այնպիսի բառերի ու արտահայտությունների զուգակցումներին, որոնք խոսքի` հոգեբանական ազդեցության դրսևորումներ են և մեծ ներուժ ունեն խթանելու ապրանքի/ ծառայության սպառումը:

The linguistic landscape in the province of Friesland is characterized by a rich diversity of language varieties: Frisian, peripheral Frisian, Frisian-Dutch contact varieties, and Low Saxon. There is often consensus about this in the literature. Within Frisian, a distinction is again made between Clay Frisian, Wood Frisian and Southwest Frisian. However, there is no consensus about an area in the northeast of the province that is referred to as North clay Frisian: does this belong to Clay Frisian, Wood Frisian, or is it an area in itself? In the 1980s Klaas van der Veen published a dialectometric study. Based on high-frequency words from various sources, he calculated distances between local Frisian varieties using Jean Séguy's method. He weighted the words he used by their frequency of use. Based on the distances, he made a classification of the Frisian dialects and found that North Clay Frisian belongs to Clay Frisian. We compared his analysis with two of our own analyses. In the first analysis we measured lexical distances using Jean Ségey's method as well, and in the second analysis we measured distances in the sound components using PMI Levenshtein distance. Our measurements were based on a random selection of words from the texts in the Reeks Nederlandse Dialectatlassen (RND). The well-known threefold division within Frisian was not found on the basis of the lexical measurements, but on the basis of the distances in the sound components we did find this division, and with North Clay Frisian forming a group with Wood Frisian. The differences between our results and Van der Veen's results are mainly determined by the choice of sources (a mix of different sources versus only the RND) and the choice of words (only high-frequency words versus a random selection from a fairly representative text).

This study aims to analyze how word choice, or diction, in the introduction of environmental awareness (eco green) initiatives influences employee behavior at Campus IBM Asmi Jakarta. Language plays a critical role in shaping attitudes and actions, particularly in the context of sustainability efforts. This research examines how the selection of words and phrases affects perception, comprehension, and engagement with environmental policies among employees. The study also discusses best practices in communication strategies to foster a more environmentally responsible workforce. Employing an ecolinguistic approach and persuasive communication theory, this research explores the role of language in enhancing pro-environmental awareness and actions within the organizational environment. The methodology includes discourse analysis of environmental campaign materials used by the institution, as well as a survey of employees regarding the impact of these communications on their behaviors. The results indicate that the use of positive and persuasive language is more effective in increasing employee participation in environmentally friendly practices than instructive or imperative language. These findings have significant implications for the development of corporate communication strategies that support environmental sustainability.

Research Objectives: The aim of this article is to analyze the ethnonyms used in Jewish texts in relation to Tartars and some other groups of the Muslim population. The article intends to present the differences in the use of these terms, as well as the factors behind their emergence and their evolution. These ethnonyms will be considered both in their linguistic and cultural-historical contexts, as these dimensions add frequently specific connotations to some of these terms. Research Materials: This study is based on specific examples of the use of terms in Jewish sources, written in Hebrew, from the Middle Ages to the beginning of the 19th century. These include texts on Jewish religious law and Kabbalah, as well as travelogues and historical chronicles written in the Crimean Khanate between the 17th and 19th centuries. Chronographic texts reflect everyday contacts and relations between Jews and Tatars, and therefore contain rich material for the study of ethnonyms. Results and Novelty of the Research: The ethnonyms used in Jewish texts in relation to the Tartars have never been studied, except regarding an explanation of the meaning of individual terms. The present research is not only about a selection of words and their etymology. The ethnonyms found in Jewish sources, related to Tartars and some other groups of the Crimean Muslim population, reveal a broader picture. They reflect some cultural and religious concepts and stereotypes towards the Tartars, as well as a view on the interethnic relations as perceived by the authors of these texts and their immediate environment.

How language constrains communication: an EEG hyperscanning study of word selection during bilingual communication