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An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging

Claire Cordella1, Manuel J Marte1, Hantian Liu1,2

  • 1Center for Brain Recovery, Boston University, MA.

Perspectives of the ASHA Special Interest Groups
|August 11, 2025
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Summary
This summary is machine-generated.

This article introduces machine learning (ML) to speech-language pathology, covering concepts, applications, and challenges. It aims to guide clinicians and researchers in responsibly using ML for better patient outcomes.

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Area of Science:

  • Speech-language pathology
  • Computational linguistics
  • Artificial intelligence

Background:

  • Machine learning (ML) offers advanced analytical capabilities.
  • The integration of ML in speech-language pathology is an emerging area.
  • Understanding ML is crucial for future research and clinical practice.

Purpose of the Study:

  • To introduce clinicians and researchers to machine learning (ML) approaches in speech-language pathology.
  • To provide an overview of key ML concepts, terminology, and models.
  • To discuss the challenges and limitations of implementing ML in the field.

Main Methods:

  • Introduction to fundamental ML concepts (e.g., classification, regression).
  • Explanation of various ML models (e.g., random forest, support vector machines, neural networks).
  • Review of six exemplar studies applying ML in aphasiology.

Main Results:

  • Demonstration of ML applications in speech-language pathology through case studies.
  • Discussion of critical considerations for ML model development and evaluation.
  • Identification of limitations including reliability, validity, bias, and explainability.

Conclusions:

  • ML holds significant potential for advancing speech-language pathology.
  • Addressing challenges in reliability, validity, and explainability is key for clinical adoption.
  • Responsible implementation of ML is essential for meaningful clinical impact.