Automated speech therapy through personalized pronunciation correction using reinforcement learning and large language models
Why this work is in the frame
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Bibliographic record
Abstract
• A novel AI based personalized pronunciation corrector is propose. • A customized Proximal Policy Optimization (PPO) defined for accurate pronunciation evaluation. • Very good accuracy obtained demonstrating the system's effectiveness. Traditional approaches to pronunciation correction often face challenges in personalization, adaptability, and consistent feedback. This study introduces a novel AI-powered system that integrates Reinforcement Learning (RL) and Large Language Models (LLMs) to address these limitations. The system employs a custom Proximal Policy Optimization (PPO) algorithm for precise pronunciation evaluation and an Large Language Models to deliver detailed, encouraging, and user-specific feedback. It was evaluated using the CMU Sphinx Dictionary dataset, a foundational phonetic resource, alongside dynamically generated user-specific session data for personalized feedback and model refinement. Further validation utilized datasets such as TIMIT, LibriTTS, SpeechOcean762, and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), enabling direct comparisons with contemporary methods. Results demonstrate the system's robustness in handling diverse phonetic variations. While primarily tested on English data, its modular architecture supports adaptation to other languages and dialects through language-specific phonetic datasets. The system achieved exceptional performance metrics: 97.9% phoneme-level accuracy, 87.7% word-level accuracy, 95.2% syllable count accuracy, and 89.4% perfect accuracy on the CMU Sphinx dataset. This innovative approach underscores the potential of advanced AI techniques to enhance the personalization and effectiveness of pronunciation correction systems. All findings are quantitatively validated and thoroughly documented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it