โก Quick Summary
This review highlights the use of automation technologies and data mining in speech recognition for identifying autism spectrum disorder (ASD). The findings indicate that automated speech analysis can achieve moderate-to-high accuracy in ASD detection, paving the way for scalable clinical applications.
๐ Key Details
- ๐ Timeframe: 1994 to 2025
- ๐งฉ Focus: Automated tools and data-mining methods for speech-based ASD assessment
- โ๏ธ Technologies Reviewed: LENA, Praat, HTK/FAVE, CMU Sphinx, Kaldi, AutoSALT, openSMILE/eGeMAPS, and more
- ๐ Performance Metrics: Moderate-to-high accuracy for ASD detection
๐ Key Takeaways
- ๐ Early identification of ASD is crucial for improving long-term outcomes.
- ๐ค Automation technologies can streamline the analysis of speech, making it more scalable.
- ๐ Data-mining methods have evolved from logistic regression to advanced models like CNN/LSTM and transformers.
- ๐ Challenges include performance variability across languages, ages, and tasks.
- ๐ Privacy and fairness remain significant barriers to the deployment of these technologies.
- ๐ Future strategies include optimizing existing tools and enabling global data sharing.
- ๐ก Cross-domain innovations can enhance the effectiveness of speech assessment tools.
๐ Background
The early identification of autism spectrum disorder (ASD) is vital for improving the quality of life for affected individuals. Traditional methods of assessment can be labor-intensive and subjective. With advancements in digital recording and artificial intelligence, there is a growing interest in using speech as a noninvasive biomarker for ASD, which could lead to more efficient and objective assessments.
๐๏ธ Study
This structured narrative review examines the methodological advancements in speech-based ASD assessment from 1994 to 2025. It focuses on the development and clinical translation of automated tools and data-mining methods, highlighting their applications in various recording settings and tasks.
๐ Results
The review found that automated indices of prosody, voice quality, linguistic content, and interactional behavior demonstrate moderate-to-high accuracy in detecting ASD and correlate meaningfully with clinician-rated severity. However, challenges such as performance degradation across different languages and ages, as well as issues related to dataset size and privacy, persist.
๐ Impact and Implications
The findings from this review suggest that the integration of automation technologies in speech analysis could significantly enhance the early detection of ASD. By addressing current limitations and focusing on scalable solutions, we can improve clinical practices and ultimately lead to better outcomes for individuals with ASD. The potential for global data sharing and cross-domain innovations could further revolutionize the field.
๐ฎ Conclusion
This review underscores the transformative potential of automation technologies and data mining in the assessment of autism through speech recognition. By optimizing existing tools and fostering collaborative efforts in data sharing, we can pave the way for more effective and accessible ASD assessments. The future of speech analysis in clinical settings looks promising, and continued research is essential to overcome existing challenges.
๐ฌ Your comments
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Use of Automation Technologies and Data Mining in Speech Recognition for Autism.
Abstract
INTRODUCTION: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSION: 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.
Author: [‘Mao R’, ‘Zhu Y’]
Journal: Brain Behav
Citation: Mao R and Zhu Y. Use of Automation Technologies and Data Mining in Speech Recognition for Autism. Use of Automation Technologies and Data Mining in Speech Recognition for Autism. 2026; 16:e71229. doi: 10.1002/brb3.71229