Editing State-of-the-art (section)

==== Schultz, B.G., Tarigoppula, V.S.A., Noffs, G. ''et al.'' Automatic speech recognition in neurodegenerative disease. ''Int J Speech Technol'' 24, 771–779 (2021). <nowiki>https://doi-org.proxy-ub.rug.nl/10.1007/s10772-021-09836-w</nowiki> ====
*Summary: The paper evaluates the performance of three state-of-the-art automatic speech recognition (ASR) platforms (Amazon Web Services, Google Cloud, and IBM Watson) on speech from individuals with neurodegenerative diseases (multiple sclerosis and Friedreich's ataxia) and healthy controls.
* RQ: How well do commercial ASR systems perform on dysarthric speech from individuals with neurodegenerative diseases compared to healthy speech?
* Hypothesis: ASR accuracy will be lower for dysarthric speech from neurodegenerative disease groups compared to healthy controls, and accuracy will decline with increased disease severity and duration.
* Conclusion: ASR accuracy was significantly higher for healthy controls than clinical groups, and higher for multiple sclerosis compared to Friedreich's ataxia. Amazon Web Services and Google Cloud outperformed IBM Watson. Accuracy decreased with increased disease duration for Friedreich's ataxia but not multiple sclerosis. Age and sex did not significantly affect ASR accuracy.
* Critical observations: 
** ASR faces challenges in recognizing dysarthric speech from neurodegenerative diseases.
** Accuracy declines as consecutive words increase, irrespective of speech impairment.
** Severity of speech impairment, as indicated by disease type and duration, negatively impacts ASR accuracy.
* Relevance: The theme focuses on low-resource ASR for underrepresented languages. While this study does not directly address low-resource languages, it highlights the challenges ASR systems face in recognizing atypical speech patterns, which is relevant for low-resource languages with diverse speaker populations and dialects. Improving ASR performance on dysarthric speech could inform techniques for handling speech variability in low-resource settings.