Application of Fuzzy Logic-Based Multi-Feature Audio Classification and Speech Matching in English Speaking Teaching and Learning
Bibliographic record
Abstract
As the material foundation of language, speech is the basis for mastering language skills and capturing language information, and English learning must begin with the correct mastery of spoken language. Therefore, spoken language teaching occupies a rather important position in English teaching. In this study, we extract various features such as time-domain features and frequency-domain features from English spoken audio signals, use fuzzy logic inference model to represent each audio feature mapping as an affiliation function, and then optimize the parameters of the affiliation function by using adaptive neuro-fuzzy inference system, and solve the affiliation function to get the result of speech matching by the center of gravity method. Subsequently, a speech evaluation system is designed based on the speech matching model to assist intelligent spoken language teaching. The results of teaching practice show that students in the experimental class using the voice assessment system as a learning aid are significantly better than the control class in terms of speaking skills and learning attitudes (P<0.05). Through real-time feedback and personalized practice, the voice assessment system enables students to correct pronunciation errors immediately and gradually improve their speaking fluency and accuracy. It can also improve students' self-efficacy and learning motivation. This study confirms the effectiveness of the fuzzy logic-based audio classification and speech matching model in improving students' spoken English proficiency and reveals its potential for wide application in future spoken English education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".