Speech pattern disorders in verbally fluent individuals with autism spectrum disorder: a machine learning analysis
These findings demonstrate that a compact, diverse set of non-MFCC and selected spectral features effectively characterizes speech abnormalities in verbally fluent individuals with ASD. The approach highlights the potential of context-aware, data-driven models to complement clinical assessments and enhance understanding of speech-related manifestations in ASD.
Authors
- Lynn K. Paul (ORCID: https://orcid.org/0000-0002-3128-8313)
- Chuanbo Hu (ORCID: https://orcid.org/0000-0003-1165-5005)
- Mindi Ruan (ORCID: https://orcid.org/0000-0002-7931-500X)
- Jacob Thrasher
- Wenqi Li (ORCID: https://orcid.org/0000-0003-3726-343X)
- Shuo Wang (ORCID: https://orcid.org/0000-0002-1390-9219)
- Xin Li (ORCID: https://orcid.org/0000-0003-0119-0072)
- Xiangxu Yu (ORCID: https://orcid.org/0000-0002-2710-2969)
Institutions
- West Virginia University (US)
- California Institute of Technology (US)
- Washington University in St. Louis (US)
- University at Albany, State University of New York (US)
Publication Details
- Journal
- Frontiers in Neuroinformatics
- Published
- 2025-10-24
- DOI
- https://doi.org/10.3389/fninf.2025.1647194
- Citations
- 2
- Primary Topic
- Autism Spectrum Disorder Research
- Type
- article
- Field-Weighted Citation Impact
- 2.86
Funders
- National Science Foundation
- University at Albany