Transforming child speech data into clinical-grade artificial intelligence pipelines for speech-language impairment detection
Bibliographic record
Abstract
Diagnosing speech-language impairments in children using AI requires an end-to-end audio processing pipeline capable of handling heterogeneous datasets, speech variability, and clinical-grade accuracy. This study presents a framework that converts raw child speech into AI-ready datasets through rigorous standardization, quality assurance, and explainability. The process begins with automated file restructuring and metadata-tagging to seamlessly integrate audio, video, and transcripts. Advanced preprocessing techniques—such as spectral noise reduction, silence normalization, and adaptive segmentation—produce clean datasets while preserving critical linguistic and acoustic features. The Montreal Forced Aligner synchronizes speech and transcripts at the phonetic level, enabling detailed speaker diarization and annotation. At its core, the AI pipeline employs fine-tuned models like Whisper for ASR and neural network classifiers trained on high-dimensional acoustic and prosodic embeddings. A custom bias analysis framework ensures fairness across diverse demographics, while explainable AI-powered phonetic grading and speech analysis deliver actionable insights for clinicians. Automated orchestration via GitHub Actions minimizes manual effort, enhancing scalability and operational efficiency. By prioritizing clinical interpretability and data security, this framework sets a new benchmark for detecting speech-language impairments, advancing speech pathology and AI research.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".