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

被引：89

作者：

Latif, Siddique ^{[1
,2
]}

Qadir, Junaid ^{[3
]}

Qayyum, Adnan ^{[3
]}

Usama, Muhammad ^{[3
]}

Younis, Shahzad ^{[4
]}

机构：

[1] Univ Southern Queensland USQ, Springfield, Qld 4350, Australia

[2] CSIRO, Distributed Sensing Syst Grp, Data61, Pullenvale, Qld 4069, Australia

[3] Informat Technol Univ, Lahore 54000, Pakistan

[4] Natl Univ Sci & Technol, Islamabad 44000, Pakistan

来源：

IEEE REVIEWS IN BIOMEDICAL ENGINEERING | 2021年 / 14卷

关键词：

Medical services; Hidden Markov models; Speech recognition; Feature extraction; Speech processing; Task analysis; Deep learning; automatic speech recognition (ASR); speech synthesis; healthcare; speech biomarkers; remote monitoring; CLINICAL DOCUMENTATION; NEURAL-NETWORK; RECOGNITION; EMOTION; SYSTEM; ASR; CLASSIFICATION; DEPRESSION; PATIENT; ATTACKS;

D O I：

10.1109/RBME.2020.3006860

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页码：342 / 356

页数：15

共 202 条

[1] Introduction of digital speech recognition in a specialised outpatient department: a case study [J].

Ahlgrim, Christoph ;

Maenner, Oliver ;

Baumstark, Manfred W. .

BMC MEDICAL INFORMATICS AND DECISION MAKING, 2016, 16 :1-8

[2]

Al-Fatlawi AH, 2016, IEEE C EVOL COMPUTAT, P1324, DOI 10.1109/CEC.2016.7743941

[3] Voice Pathology Detection Using Deep Learning on Mobile Healthcare Framework [J].

Alhussein, Musaed ;

Muhammad, Ghulam .

IEEE ACCESS, 2018, 6 :41034-41041

[4] An Automatic Health Monitoring System for Patients Suffering From Voice Complications in Smart Cities [J].

Ali, Zulfiqar ;

Muhammad, Ghulam ;

Alhamid, Mohammed F. .

IEEE ACCESS, 2017, 5 :3900-3908

[5] A Mobile Multi-Technology Gateway to Enable IoT Interoperability [J].

Aloi, G. ;

Caliciuri, G. ;

Fortino, G. ;

Gravina, R. ;

Pace, P. ;

Russo, W. ;

Savaglio, C. .

PROCEEDINGS 2016 IEEE FIRST INTERNATIONAL CONFERENCE ON INTERNET-OF-THINGS DESIGN AND IMPLEMENTATION IOTDI 2016, 2016, :259-264

[6]

Alzantot M., 2018, NIPS 2017 Machine Deception Workshop

[7] A review of smart homes in healthcare [J].

Amiribesheli, Mohsen ;

Benmansour, Asma ;

Bouchachia, Abdelhamid .

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2015, 6 (04) :495-517

[8] Recognition of Echolalic Autistic Child Vocalisations Utilising Convolutional Recurrent Neural Networks [J].

Amiriparian, Shahin ;

Baird, Alice ;

Julka, Sahib ;

Alcorn, Alyssa ;

Ottl, Sandra ;

Petrovic, Suncica ;

Ainger, Eloise ;

Cummins, Nicholas ;

Schuller, Bjoern .

19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6: SPEECH RESEARCH FOR EMERGING MARKETS IN MULTILINGUAL SOCIETIES, 2018, :2334-2338

[9]

[Anonymous], 2018, ABS180106353 CORR

[10]

[Anonymous], 2001, P 17 EUR C SPEECH CO

← 1 2 3 4 5 6 7 8 9 10 →