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Automatic Speech Recognition (ASR) in medical contexts has the potential to save time, cut costs, increase report accuracy, and reduce physician burnout.
BERTScore: Evaluating Text Generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2019 · 1904
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Is word error rate a good indicator for spoken language understanding accuracy
Ye-Yi Wang, A. Acero, and C. Chelba. 2003 · 2003
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From wer and ril to mer and wil: improved evaluation measures for connected speech recognition
Andrew Cameron Morris, Viktoria Maier, and Phil Green. 2004 · 2004
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
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Why word error rate is not a good metric for speech recognizer training for the speech translation task?
Xiaodong He, Li Deng, and Alex Acero. 2011 · 2011
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Building large monolingual dictionaries at the Leipzig corpora collection: From 100 to 200 languages
Dirk Goldhahn, Thomas Eckart, and Uwe Quasthoff. 2012 · 2012
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Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine
Ming Tai-Seale, Cliff W Olson, Jinnan Li, Albert S Chan, Criss Morikawa, Meg Durbin, Wei Wang, and Harold S Luft. 2017 · 2017
Cited alongside, same era.
To care is human—collectively confronting the clinician-burnout crisis
Victor J Dzau, Darrell G Kirch, Thomas J Nasca, et al. 2018 · 2018
Cited alongside, same era.
Towards Learning a Universal Non-Semantic Representation of Speech
Joel Shor, Aren Jansen, Ronnie Maor, Oran Lang, Omry Tuval, Félix de Chaumont Quitry, Marco Tagliasacchi, Ira Shavitt, Dotan Emanuel, and Yinnon Haviv. 2020 · 2020
Cited alongside, same era.
Speech technology for healthcare: Opportunities, challenges, and state of the art
Siddique Latif, Junaid Qadir, Adnan Qayyum, Muhammad Usama, and Shahzad Younis. 2021 · 2021
Cited alongside, same era.
FRILL: A non-semantic speech embedding for mobile devices
Jacob Peplinski, Joel Shor, Sachin Joglekar, Jake Garrison, and Shwetak Patel. 2021 · 2021
Cited alongside, same era.
Universal paralinguistic speech representations using self-supervised conformers
Joel Shor, Aren Jansen, Wei Han, Daniel Park, and Yu Zhang. 2022 · 2022
Later among the works it cites.
TRILLsson: Distilled universal paralinguistic speech representations
Joel Shor and Subhashini Venugopalan. 2022 · 2022
Later among the works it cites.
Assessing ASR Model Quality on Disordered Speech using BERTScore
Jimmy Tobin, Qisheng Li, Subhashini Venugopalan, Katie Seaver, Richard Cave, and Katrin Tomanek. 2022 · 2022
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2023
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Introducing the knowledge graph: things, not strings
Amit Singhal. 2012 · 2023
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The medical scribe: Corpus development and model performance analyses
Izhak Shafran, Nan Du, Linh Tran, Amanda Perry, Lauren Keyes, Mark Knichel, Ashley Domin, Lei Huang, Yu-hui Chen, Gang Li, Mingqiu Wang, Laurent El Shafey, Hagen Soltau, and Justin Stuart Paul. 2020 · 2044
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Understanding medical conversations: Rich transcription, confidence scores & information extraction
Hagen Soltau, Mingqiu Wang, Izhak Shafran, and Laurent El Shafey. 2021 · 2021
Cited alongside, same era.
Comparing Supervised Models and Learned Speech Representations for Classifying Intelligibility of Disordered Speech on Selected Phrases
Subhashini Venugopalan, Joel Shor, Manoj Plakal, Jimmy Tobin, Katrin Tomanek, Jordan R. Green, and Michael P. Brenner. 2021 · 2021
Cited alongside, same era.
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