Literature DB >> 32746367

Speech Technology for Healthcare: Opportunities, Challenges, and State of the Art.

Siddique Latif, Junaid Qadir, Adnan Qayyum, Muhammad Usama, Shahzad Younis.   

Abstract

Speech technology is not appropriately explored even though modern advances in speech technology-especially those driven by deep learning (DL) technology-offer unprecedented opportunities for transforming the healthcare industry. In this paper, we have focused on the enormous potential of speech technology for revolutionising the healthcare domain. More specifically, we review the state-of-the-art approaches in automatic speech recognition (ASR), speech synthesis or text to speech (TTS), and health detection and monitoring using speech signals. We also present a comprehensive overview of various challenges hindering the growth of speech-based services in healthcare. To make speech-based healthcare solutions more prevalent, we discuss open issues and suggest some possible research directions aimed at fully leveraging the advantages of other technologies for making speech-based healthcare solutions more effective.

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Year:  2021        PMID: 32746367     DOI: 10.1109/RBME.2020.3006860

Source DB:  PubMed          Journal:  IEEE Rev Biomed Eng        ISSN: 1937-3333


  4 in total

1.  Automatic Speech Recognition Method Based on Deep Learning Approaches for Uzbek Language.

Authors:  Abdinabi Mukhamadiyev; Ilyos Khujayarov; Oybek Djuraev; Jinsoo Cho
Journal:  Sensors (Basel)       Date:  2022-05-12       Impact factor: 3.847

2.  Designing Virtual Reality-Based Conversational Agents to Train Clinicians in Verbal De-escalation Skills: Exploratory Usability Study.

Authors:  Nathan Moore; Naseem Ahmadpour; Martin Brown; Philip Poronnik; Jennifer Davids
Journal:  JMIR Serious Games       Date:  2022-07-06       Impact factor: 3.364

3.  How do people think about the implementation of speech and video recognition technology in emergency medical practice?

Authors:  Ki Hong Kim; Ki Jeong Hong; Sang Do Shin; Young Sun Ro; Kyoung Jun Song; Tae Han Kim; Jeong Ho Park; Joo Jeong
Journal:  PLoS One       Date:  2022-09-23       Impact factor: 3.752

4.  An Attention Mechanism Oriented Hybrid CNN-RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction.

Authors:  Bin Li; Yuqing He
Journal:  Comput Intell Neurosci       Date:  2021-07-08
  4 in total

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