Description:Description of the Related Art
[0002] The skill of speaking is seen as a crucial sign of communicative ability in language learning, especially when it comes to English as a Second or Foreign Language (ESL/EFL). Pronunciation, grammatical precision, lexical resource (vocabulary), and the capacity to engage in natural conversation are all interconnected elements that make up fluency. Together, these factors affect a learner's ability to communicate in real time by sending and receiving messages. Standardized speaking exams, oral interviews, and holistic evaluations have all been major components of traditional speaking skills assessment approaches. Although these techniques offer valuable insights, they frequently lack granularity and provide imprecise measures of the distinct elements that make up fluency. Furthermore, it could be challenging to track learners' progress over time, and these tests could be biased by examiners.
[0003] Analytical approaches to speaking skills testing are becoming more popular as a result of recent developments in applied linguistics and educational evaluation. In particular, quantitative comparisons of pre-test and post-test results enable researchers and educators to more precisely assess the efficacy of educational interventions. It is feasible to pinpoint certain areas of difficulty or progress for every student by breaking down speaking skills into quantifiable categories, such as fluency, vocabulary variety and appropriateness, grammatical accuracy, pronunciation clarity, and interactional competence. In addition to improving diagnostic accuracy, this analytical method facilitates focused instructional planning and tailored feedback.
[0004] The possibility of a more methodical and data-driven evaluation has increased with the incorporation of digital tools and learning analytics into language assessment. It is now possible to evaluate pronunciation and fluency components in real-time using recorded speech samples thanks to computer-assisted language learning (CALL) systems and automatic speech recognition (ASR) technology. Pre- and post-testing becomes more dependable and scalable when these systems include scoring algorithms that can monitor improvement across multiple fluency metrics. A significant move toward evidence-based language assessment is represented in this context by a method that employs pre- and post-scores across specified fluency measures, such as pronunciation, accuracy, vocabulary, and interaction.
[0005] In outcome-based education systems, where quantifiable learning outcomes are essential for curriculum development and quality control, the significance of this approach is particularly pertinent. It fits in nicely with modern teaching strategies that prioritize formative evaluation, learner autonomy, and ongoing assessment. Additionally, this approach has useful advantages for teachers who work with sizable and heterogeneous student bodies, where logistically difficult tailored evaluation is frequently the case. The need for systematic, impartial, and repeatable techniques to assess speaking abilities is rising as language teachers work to increase student fluency through focused instruction and adaptive feedback. Thus, an important vacuum in language testing and pedagogy can be filled by an approach that systematically assesses and compares fluency before and after training across many linguistic domains.
SUMMARY
[0001] In view of the foregoing, an embodiment herein provides a method for detecting speaking skills using pre and post scores of fluency, pronunciation, accuracy, vocabulary, interaction. In some embodiments, wherein an organized and data-driven method for assessing language learners' progress in speaking, especially in second language acquisition environments, is provided by "A Method for Detecting Speaking Skill Test Using Pre and Post Scores of Fluency, Pronunciation, Accuracy, Vocabulary, and Interaction." Fluency, Pronunciation, accuracy, vocabulary, and interaction are five crucial linguistic characteristics that are thought to be essential markers of oral competency. This approach compares pre-test and post-test performance measures in these areas. The approach is intended to offer a trustworthy and impartial framework for determining how learners' fluency levels evolve over time. Teachers and evaluators can assess the efficacy of teaching strategies, learning interventions, or language programs by gathering and comparing quantitative scores before and after a certain instructional or practice time. By capturing individual linguistic regressions or improvements, the technique provides fine-grained insights into the areas in which a learner is succeeding or requires additional support.
[0002] In some embodiments, whereas the structured observation methods, computerized or semi-automatic evaluation tools, and defined grading rubrics are all used in the detection process. Differential scores that indicate advancements or lack thereof in each of the four domains are produced by the comparison analysis. Individual student profiles, group progress reports, or aggregated statistics for curriculum review are several ways to visualize these outcomes.
[0003] In some embodiments, wherein in this approach's versatility across a range of learning contexts, such as blended or hybrid models, digital learning platforms, and classroom-based education, is one of its main advantages. By highlighting students' strengths and shortcomings using real data, it makes tailored feedback easier. Additionally, the approach lowers subjective bias and guarantees uniform evaluation standards while improving the transparency and accountability of fluency testing. Teachers, language instructors, curriculum designers, and educational researchers who want to promote more efficient language acquisition techniques may find this method especially helpful. Additionally, it facilitates institutional decision-making on learner placement, program accreditation, and innovative teaching. This approach promotes reflective teaching and learner self-evaluation in addition to measuring outcome-based learning by placing a strong emphasis on pre- and post-assessment comparisons. In the end, it supports a learner-cantered, more empirically supported method of fostering fluency in language instruction.
[0004] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS
[0001] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0002] FIG. 1 illustrates a method for detecting speaking skills using pre and post scores of fluency, pronunciation, accuracy, vocabulary, interaction according to an embodiment herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0001] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0002] FIG. 1 illustrates a method for detecting speaking skills using pre and post scores of fluency, pronunciation, accuracy, vocabulary, interaction according to an embodiment herein. In some embodiments, the fundamental components of speaking skill are these competencies: Fluency, interaction, vocabulary, pronunciation, and accuracy. By combining data-driven computational methods with psycholinguistic evaluation theories, the system assesses students' performance over time, enabling teachers and systems to identify notable improvement in speaking skill or stagnation. Initial learner profiling, baseline score assessment (pre-test), instructional or interactive exposure phase, post-test evaluation, data normalization and scoring, delta analysis, and interpretative reporting for scholarly and pedagogical use are the steps in the methodology's systematic cycle. Learner profiling is the first stage of the system, during which demographic and linguistic background information is gathered. This comprises details like the learner's age, native tongue, English proficiency level as measured by the CEFR or an institutional scale, learning environment, and prior exposure to spoken English. By contextualizing the fluency detection paradigm, this profiling makes sure that learner-specific factors that could impact language performance like accent interference or cognitive bias in language retention are taken into consideration in comparisons between assessments before and after. At this point, machine learning techniques can be used to group students into typological clusters, then modify the test material appropriately.
[0003] In some embodiments, the system moves into the pre-assessment phase after learner profile, where baseline scores are set for fluency, vocabulary, pronunciation, accuracy, and interactivity. Every one of these five areas has sub-criteria. In terms of pronunciation, emphasis is placed on intonation, rhythm, stress, and phoneme articulation. The learner's speech is assessed by human raters or automated speech recognition (ASR) systems using native-like standards. Grammatical correctness and syntactic coherence in the speech are taken into account while evaluating accuracy. It records mistakes in sentence construction, article placement, tense usage, and subject-verb agreement. Lexical range, word choice, collocational power, and usage of colloquial idioms are all used to gauge vocabulary. Turn-taking, response, conversational repair techniques, and interaction with the other person are all evaluated in the interaction component.
[0004] In some embodiments, the task-based exercises and prompt-driven oral evaluations are both used in the pre-test. Students may be required to participate in brief conversations, impromptu answers to questions, and monologues. While voice is maintained for phonetic evaluation, all speech samples are gathered via a standardized interface and transcribed using natural language processing (NLP) techniques for textual analysis. Based on predetermined rubrics, instructors or AI tools issue scores. These are often in the form of Likert scales or numerical ratings (e.g., 0–5 or 0–10) for each component.
[0005] In some embodiments, the educational intervention phase is the next stage of the workflow, and its length varies according on the pedagogical approach used. In this stage, students participate in focused speaking exercises intended to enhance one or more of the five fundamental elements of speaking skill. These could consist of communicative group activities, vocabulary enrichment modules, grammatical correction sessions, and pronunciation drills. Artificial intelligence (AI)-based solutions can be connected to give learners real-time feedback, such as providing synonyms and collocations in context or warning them when a pronunciation variation occurs. In order to customize training, teachers can also utilize structured feedback forms that are based on the pre-test results. Students complete a post-assessment at the end of the class period that follows the same format as the pre-assessment. To guarantee comparability, the same or parallel speech tasks are given. To prevent memorized answers, the post-assessment might ask a student to describe a typical weekend, which would be similar in complexity but not exactly the same as a response to a previous prompt about describing a favourite hobby. Each learner receives a unique set of ratings based on the evaluation of fluency, pronunciation, accuracy, vocabulary, and interaction using the same rubric.
[0006] In order to guarantee impartial and objective comparisons between pre- and post-scores, the data standardization and scoring stage is essential.
[0007] This methodology's incorporation of interactional competence as a fundamental area of fluency assessment is one of its innovative features. In conventional speaking assessments, interaction is frequently disregarded, despite the fact that it is essential to everyday communication. The system uses discourse analysis methods and records live peer-to-peer or learner-instructor talks to evaluate this dimension. Quantified features include backchannels, filler usage, average turn duration, inter-turn response delay, and topic maintenance techniques. An interaction score is then created by combining these metrics. To assess a learner's capacity to maintain conversation flow, systems with dialogue modeling AI can also mimic interaction with virtual agents.
[0008] The creation of interpretative reports and feedback is the last phase of the working process. Comprehensive reports that indicate areas of growth, stagnation, and decline are sent to educators and learners. These reports frequently combine quantitative scores with qualitative comments. A learner might be told, for example, that although their vocabulary grew, their interactional fluency stayed the same since they frequently hesitated or had trouble taking turns. Teachers can utilize these observations to improve their teaching methods, create individualized lesson plans, or assign students to groups for specific speaking exercises.
[0009] Learning analytics dashboards can be used to scale the approach in research or teaching contexts. By combining fluency data from several students and cohorts, these dashboards enable administrators to track patterns in language learning, identify common issues, and assess how well speaking courses are working. Using predictive modeling to identify learners at risk of stagnation or clustering learners according to fluency characteristics are examples of advanced analytics. Throughout the process, security, privacy, and ethical compliance are upheld. Assessment data and voice recordings are kept in encrypted formats and managed in compliance with institutional IRB guidelines or data protection laws like the GDPR. When using data for study, anonymization measures are used, and learners give their informed agreement to participate.
[0010] It can be used in mixed learning settings, online learning environments, or traditional classroom settings. The approach uses desktop or mobile apps that record and send learner speech to cloud-based processing systems in remote learning scenarios. Offline versions of the system with embedded AI models and simple analytics tools can be used to maintain continuity in environments with restricted resources. The approach is perfect for semester-long or year-long language programs because it allows for longitudinal tracking of student fluency. By visualizing fluency trajectories over time, teachers can take proactive measures to help students who exhibit plateauing. By mapping internal scores to expected band descriptors, the system can be adjusted to conform to international standards such as the TOEFL or IELTS, promoting standard benchmarking and transferability.
[0011] Thus, this all-inclusive strategy offers a solid, data-driven, and pedagogically sound method for detecting speaking skills. It ensures a comprehensive perspective of learner performance by addressing both macro-level communicative abilities (like interaction and discourse management) and micro-level language features (like phoneme articulation and syntactic control). It emphasizes progress over static proficiency by introducing a dynamic measurement aspect through its focus on pre- and post-assessment comparison. By combining AI, psycholinguistic theory, and language education, this approach improves student engagement, instructional accuracy, and result accountability in addition to quantifying fluency. , Claims:I/We Claim:
1. A method for detecting speaking skills using pre and post scores of fluency, pronunciation, accuracy, vocabulary, interaction, wherein the method comprising:
collecting pre-test scores from learners across five key parameters: fluency, pronunciation, accuracy, vocabulary, and interaction to establish a baseline speaking level;
conducting a controlled speaking skill test session with standardized prompts to evaluate the natural speaking capabilities of learners in a real-time or simulated environment;
measuring post-test scores using the same parameters fluency, pronunciation, accuracy, vocabulary, and interaction—to capture changes in speaking performance;
comparing the pre- and post-test scores through a statistical or algorithmic framework to detect improvements or regressions in individual speaking skill components;
generating a comprehensive speaking skill report by synthesizing differences in the measured scores and mapping them to CEFR (Common European Framework of Reference) or equivalent levels; and
providing actionable insights or feedback for learners and instructors based on the comparative results to guide future language training strategies and interventions.