Semi-Supervised Acoustic Model Retraining for Medical ASR

Finley, Greg; Edwards, Erik; Salloum, Wael; Robinson, Amanda; Sadoughi, Najmeh; Axtmann, Nico; Korenevsky, Maxim; Brenndoerfer, Michael; Miller, Mark A.; Suendermann-Oeft, David

doi:10.1007/978-3-319-99579-3_19

Cited by 2 publications

(1 citation statement)

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“…Basically, SR-oriented and SS-oriented studies attempt to build automatic computer systems for interconversion between speech and text in the area of smart healthcare, making human-machine interaction as natural and flexible as human-human interaction [115]. For speech recognition, these efforts encompass the improvement in acoustic modelling [116], [117], language modelling [118], and the whole system pipeline [119], [120] to enhance recognition accuracy. For speech synthesis, recent advancements have been made in investigating and making synthesized speech natural [121], [122], intelligible [123]- [127] and expressive [128]- [130], which will help stimulate the enthusiasm of human-machine interaction [131].…”

Section: Nlp Approachmentioning

confidence: 99%

Natural Language Processing for Smart Healthcare

Zhou¹,

Yang²,

Shi³

et al. 2021

Preprint

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Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language. In this work we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application. We focus on feature extraction and modelling for various NLP tasks encountered in smart healthcare from a technical point of view. In the context of smart healthcare applications employing NLP techniques, the elaboration largely attends to representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss the limitations of current works and identify the directions for future works.

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