Transforming child speech data into clinical-grade artificial intelligence pipelines for speech-language impairment detection

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.