Creation of An Intelligent System for Uzbek Language Teaching Using Phoneme-Based Speech Recognition
Bibliographic record
Abstract
The recent surge in interest to learn the Uzbek language among foreigners has underscored the need for innovative teaching tools. Despite the limited studies on intelligent systems for phonemic speech recognition in the Uzbek context, this research aimed to address this gap. The purpose of this study was to create an intelligent system for teaching the Uzbek language as a foreign language based on the technology of phonemic recognition of speech signals. It was developed an intelligent system for Uzbek language instruction using phonemic speech recognition technology. The approach utilized various methods, including pinpointing challenging phonemes, comparative data analyses, and analytical-synthetic breakdowns of linguistic components, all enhanced by the wavelet transform's signal refinement. The system's precision in recognizing speech signals phoneme-by-phoneme, emphasizing difficult sounds for learners, promises broader AI-driven language study applications. Specifically designed for the Uzbek language, the system achieves an accuracy range of 67% to 95%. This breakthrough not only propels AI-driven language processing but offers a robust tool for improving Uzbek language instruction, especially beneficial for the Turkic language group. Future avenues include its use in computer modeling and automatic speech processing for Turkic languages, solidifying its innovative contribution to AI-driven language teaching.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".