A systematic review of speech recognition technology in health care

BACKGROUND To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care. METHODS A systematic review of existing literature from 2000 was undertaken. Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes. Experimental and non-experimental designs were considered. Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references. Fourteen studies met the inclusion criteria and were retained. RESULTS The heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes. CONCLUSIONS SR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns.Funding for this study was provided by the University of Western Sydney. NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program. NICTA is also funded and supported by the Australian Capital Territory, the New South Wales, Queensland and Victorian Governments, the Australian National University, the University of New South Wales, the University of Melbourne, the University of Queensland, the University of Sydney, Griffith University, Queensland University of Technology, Monash University and other university partners

The Army word recognition system

The application of speech recognition technology in the Army command and control area is presented. The problems associated with this program are described as well as as its relevance in terms of the man/machine interactions, voice inflexions, and the amount of training needed to interact with and utilize the automated system

Medical Transcriptionist’s Experience with Speech Recognition Technology

The medical transcription industry is rapidly evolving in terms of services and revenues for the last decade. This ITES contributed the largest employment growth rate in IT-BPO in 2013. The success of this industry was assisted by recent technology like Speech Recognition Technology (SRT). Because such technologies depend on people, there is a need to study on the experiences of the people behind those achievements. This paper addresses this gap by exploring Medical Transcriptionist’s (MTs) experiences using SRT. Findings revealed at least five themes prevalent to the experiences of MTs including audio file classification, valuable characteristics, negative observations, technostress coping, and highest quality orientation. This paper suggests that by looking at the experiences of MTs, current and future employers can gain insights in improving and enriching these outsourcing services. Furthermore, the presence of common themes indicates the possibility of performing a grounded theory based on substantive area of medical transcription

Improving English Pronunciation with AI Speech-Recognition Technology

This study explores the use of AI technology in the Google Read Along application as a tool to improve English pronunciation, particularly for students who struggle with English pronunciation. Therefore, the purpose of this study is to evaluate the Google Read Along app's effectiveness in improving English pronunciation, analyze the students' responses to using Google Read Along, and discover the factors that help students succeed in improving their pronunciation. Read Aloud is used in conjunction with AI technology to help children learn by listening to and precisely repeating new words and phrases. A quasi-experimental method was used to collect data, with 35 students in the experimental group and 35 in the control group. A questionnaire was presented to the experimental group regarding how they responded to Google Read Along, and interviews were conducted as further information to identify the factors affecting their pronunciation improvement. The results of the N-Gain test show that the Google Read Along is efficient in helping students improve their English pronunciation when used in combined with the Read Aloud approach by an average of 65.73 percent. As a result, a teaching strategy that combines the Read Aloud method and AI Google Read Along can be an effective alternative. Additionally, the instant feedback offered by this application gives students a chance to recognize their errors directly, and the convenience of using the application for learning anytime anywhere has a significant impact on their success in improving their pronunciation

Speech Recognition technology from the ICSI Realization Group

Summary of potential applications of the International Computer Science Institute's speech recognition work to scalable and mobile networking

Challenges in creating speech recognition for endangered language CALL: A Chickasaw case study

Speech recognition technology is increasingly becoming an important component of Computer Assisted Language Learning (CALL) software, as well as of a language’s digital vitality. CALL software that integrates speech recognition allows learners to practice oral skills without live instruction and receive feedback on pronunciation. This speech recognition technology may be particularly beneficial for endangered or under-resourced languages. Chickasaw is an indigenous language of North America now spoken mainly in the state of Oklahoma. It is estimated that there are fewer than 75 native speakers of the language remaining, though recent years have seen a surge of interest in Chickasaw culture and language revitalization. In 2007, the Chickasaw Nation launched a robust and multifaceted revitalization program, and in 2015 they commissioned CALL software that integrates speech recognition. However, creating a quality automatic speech recognition (ASR) system necessitates a number of resources that are not always readily available for endangered languages like Chickasaw. Modern speech recognition technology is based on large-scale statistical modeling of target language text and hand transcribed audio corpora. Such technology also assumes a single standardized phonetic orthography where speech can be directly mapped to text. Currently, most available resources for building speech recognition technology are based on languages where researchers are able to access a large pool of literate native speakers who are willing and able to record many hours of high quality audio, and where large volumes of accessible text already exist. For many endangered languages, these criteria cannot easily be fulfilled. This paper is focused on identifying the dimensions of resource challenges that affect building corpora for such languages, using Chickasaw as a case study. Furthermore, we identify techniques that we have used to create a corpus of speech data suitable for building an instructional speech recognition module for use in CALL software

Speech Recognition Technology: Improving Speed and Accuracy of Emergency Medical Services Documentation to Protect Patients

Because hospital errors, such as mistakes in documentation, cause one sixth of the deaths each year in the United States, the accuracy of health records in the emergency medical services (EMS) must be improved. One possible solution is to incorporate speech recognition (SR) software into current tools used by EMS first responders. The purpose of this research was to determine if SR software could increase the efficiency and accuracy of EMS documentation to improve the safety for patients of EMS. An initial review of the literature on the performance of current SR software demonstrated that this software was not 99% accurate and therefore, errors in the medical documentation produced by the software could harm patients. The literature review also identified weaknesses of SR software that could be overcome so that the software would be accurate enough for use in EMS settings. These weaknesses included the inability to differentiate between similar phrases and the inability to filter out background noise. To find a solution, an analysis of natural language processing algorithms showed that the bag-of-words post processing algorithm has the ability to differentiate between similar phrases. This algorithm is the best suited for SR applications because it is simple yet effective compared to machine learning algorithms that required a large amount of training data. The findings suggested that if these weaknesses of current SR software are solved, then the software would potentially increase the efficiency and accuracy of EMS documentation. Further studies should integrate the bag-of-words post processing method into SR software and field test its accuracy in EMS settings.https://scholarscompass.vcu.edu/uresposters/1273/thumbnail.jp

Speech Recognition Technology: Improving Speed and Accuracy of Emergency Medical Services Documentation to Protect Patients

Because hospital errors, such as mistakes in documentation, cause one in six deaths each year in the United States, the accuracy of health records in the emergency medical services (EMS) must be improved. One possible solution is to incorporate speech recognition (SR) software into current tools used by EMS first responders. The purpose of this research was to determine if SR software could increase the efficiency and accuracy of EMS documentation to improve the safety of patients of EMS. An initial review of the literature on the performance of current SR software demonstrated that this software was not 99% accurate, and therefore, errors in the medical documentation produced by the software could harm patients. The literature review also identified weaknesses of SR software that could be overcome so that the software would be accurate enough for use in EMS settings. These weaknesses included the inability to differentiate between similar phrases and the inability to filter out background noise. To find a solution, an analysis of natural language processing algorithms showed that the bag-of-words post processing algorithm has the ability to differentiate between similar phrases. This algorithm is best suited for SR applications because it is simple yet effective compared to machine learning algorithms that required a large amount of training data. The findings suggested that if these weaknesses of current SR software are solved, then the software would potentially increase the efficiency and accuracy of EMS documentation. Further studies should integrate the bag-of-words post processing method into SR software and field test its accuracy in EMS settings

Adoption of speech recognition technology in community healthcare nursing

© 2016 IMIA and IOS Press. Adoption of new health information technology is shown to be challenging. However, the degree to which new technology will be adopted can be predicted by measures of usefulness and ease of use. In this work these key determining factors are focused on for design of a wound documentation tool. In the context of wound care at home, consistent with evidence in the literature from similar settings, use of Speech Recognition Technology (SRT) for patient documentation has shown promise. To achieve a user-centred design, the results from a conducted ethnographic fieldwork are used to inform SRT features; furthermore, exploratory prototyping is used to collect feedback about the wound documentation tool from home care nurses. During this study, measures developed for healthcare applications of the Technology Acceptance Model will be used, to identify SRT features that improve usefulness (e.g. increased accuracy, saving time) or ease of use (e.g. lowering mental/physical effort, easy to remember tasks). The identified features will be used to create a low fidelity prototype that will be evaluated in future experiments

Speech recognition technology in radiology

Speech recognition devices are promising tools for the healthcare system. Speech recognition technology has had a relatively long history of use in Western healthcare systems since the 1970s. However, it became widely used at the beginning of the 21st century, replacing medical transcriptionists. This technology is relatively new in home healthcare. Its active development began only in the early 2010s, and its implementation in healthcare started in late 2010. This delay is due to the idiosyncrasies of the Russian language and the limited computational power present at the beginning of the 21st century. Currently, complexes of devices and software for speech recognition are used in the voice filling of medical records and can reduce the time for preparing reports for radiological examinations compared with traditional (keyboard) text input. The literature review provides a brief history of speech recognition technology development and application in radiography. Key scientific studies showing its efficacy in Western healthcare systems are reflected. Voice recognition technology in the home is demonstrated, and its effectiveness is evaluated. The prospects for further development of this technology in Russian healthcare are described