Use of Automation Technologies and Data Mining in Speech Recognition for Autism

Early identification of autism spectrum disorder (ASD) is critical for improving long-term outcomes, and speech offers a noninvasive source of clinically relevant biomarkers. However, manual speech analysis is time-consuming and difficult to scale. With advances in digital recording, signal processing, and artificial intelligence, researchers have increasingly deployed automated tools and data-mining methods to characterize speech and language in ASD.

This structured narrative review summarizes methodological developments in speech-based ASD assessment from 1994 to 2025, spanning diverse tasks and recording settings and focusing on automated tools, data-mining methods, and their clinical translation. We first consider core automated toolchains, including LENA, Praat, HTK/FAVE, CMU Sphinx, Kaldi, AutoSALT, openSMILE/eGeMAPS, diarization systems, and foundation-model ASR systems (e.g., Whisper), as well as modern self-supervised encoders such as wav2vec 2.0 and TRILLsson. Their typical use cases, psychometric properties, and limitations are highlighted. We then chart the progression of data-mining and machine-learning approaches from early logistic regression and clustering, through regularized regression, SVMs, and tree ensembles, to CNN/LSTM sequence models and transformer-based text and speech models (e.g., BERT, LLMs).

Across these stages, automated indices of prosody, voice quality, linguistic content, and interactional behavior show moderate-to-high accuracy for ASD detection and meaningful associations with clinician-rated severity. Nonetheless, various problems persist: performance often degrades across languages, ages, tasks, and recording settings; evaluation and reporting remain heterogeneous; datasets are typically small and single-site; and privacy, fairness, interpretability, and computational efficiency pose persistent barriers to deployment, highlighting the need for target-context benchmarking and pre-specified evaluation/reporting.

We outline three priority strategies to guide future work toward scalable, clinically credible ASD speech assessment and longitudinal monitoring: optimize and integrate existing toolchains, enable global yet privacy-preserving data sharing, and leverage cross-domain innovations in enhancement, label efficiency, and explainable, edge-ready AI.