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

Abstract

Automatic speech recognition (ASR) is increasingly becoming an integral component of our daily lives. This trend is in large part due to recent advances in machine learning, and specifically in deep learning, that have led to accurate ASR across numerous tasks. This has led to renewed interest in providing technological support to populations whose speech patterns are atypical, including identifying the presence of a specific pathology and its severity, comparing speech characteristics before and after a surgery and enhancing the quality of life of individuals with speech pathologies. The purpose of this primer is to bring readers with relatively little technical background up to speed on fundamentals and recent advances in ASR. It presents a detailed account of the anatomy of modern ASR, with examples of how it has been used in speech-language pathology research.

Keywords:

• automatic speech recognition
• assessment of speech intelligibility
• monitoring
• speech pathologies

Acknowledgements

I would like to thank Emily Mower Provost, University of Michigan, for the discussions and the help in preparing the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.