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Automatic speech recognition: A primer for speech-language pathology researchers.

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Recent advances in machine learning have improved automatic speech recognition (ASR). This technology offers new ways to support individuals with atypical speech patterns and aid speech-language pathology research.

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Area of Science:

  • Speech Technology
  • Machine Learning
  • Computational Linguistics

Background:

  • Automatic speech recognition (ASR) is increasingly integrated into daily life due to machine learning advancements.
  • Deep learning has significantly improved ASR accuracy across various tasks.
  • This has spurred interest in applying ASR to atypical speech patterns.

Purpose of the Study:

  • To provide a foundational understanding of ASR for readers with limited technical backgrounds.
  • To review recent advancements in ASR technology.
  • To illustrate ASR applications in speech-language pathology research.

Main Methods:

  • The primer details the architecture of modern ASR systems.
  • It explains the underlying machine learning and deep learning principles.
  • Examples of ASR implementation in speech research are provided.

Main Results:

  • Modern ASR systems, powered by deep learning, achieve high accuracy.
  • ASR can be utilized for identifying speech pathologies and assessing severity.
  • The technology facilitates comparisons of speech characteristics pre- and post-intervention.

Conclusions:

  • ASR technology holds significant potential for assisting individuals with speech disorders.
  • Further research integrating ASR can enhance speech-language pathology practices.
  • Accessible explanations of ASR are crucial for broader adoption and understanding.