Speech recognition for medical documentation: an analysis of time, cost efficiency and acceptance in a clinical setting

Background/Aims Medical documentation is an important and unavoidable part of a health professional's working day. However, the time required for medical documentation is often viewed negatively, particularly by clinicians with heavy workloads. Digital speech recognition has become more prevalent and is being used to optimise working time. This study evaluated the time and cost savings associated with speech recognition technology, and its potential for improving healthcare processes. Methods Clinicians were directly observed while completing medical documentation. A total of 313 samples were collected, of which 163 used speech recognition and 150 used typing methods. The time taken to complete the medical form, the error rate and error correction time were recorded. A survey was also completed by 31 clinicians to gauge their level of acceptance of speech recognition software for medical documentation. Two-sample t-tests and Mann–Whitney U tests were performed to determine statistical trends and significance. Results On average, medical documentation using speech recognition software took just 5.11 minutes to complete the form, compared to 8.9 minutes typing, representing significant time savings. The error rate was also found to be lower for speech recognition software. However, 55% of clinicians surveyed stated that they would prefer to type their notes rather than use speech recognition software and perceived the error rate of this software to be higher than typing. Conclusions The results showed that there are both temporal and financial advantages of speech recognition technology over text input for medical documentation. However, this technology had low levels of acceptance among staff, which could have implications for the uptake of this method.

Application of Fuzzy Mathematics Algorithm in Computer Speech Recognition

Speech recognition, as one of the key artificial intelligence technologies in modern development, plays an important role in any aspect. However, there are still problems in practical application, such as poor anti-interference and low degree of perfection. Therefore, this paper aims at the content of existing computer speech recognition technology, grasps fuzzy mathematical algorithm, and analyzes how to use this algorithm to better study computer speech recognition.

Intelligent Correction System of Students’ English Pronunciation Errors Based on Speech Recognition Technology

With the increasingly frequent international exchanges, English has become a common language for communication between countries. Under this research background, in order to correct students’ wrong English pronunciation, an intelligent correction system for students’ English pronunciation errors based on speech recognition technology is designed. In order to provide a relatively stable hardware correction platform for voice data information, the sensor equipment is optimized and combined with the processor and intelligent correction circuit. On this basis, the MLP (Multilayer Perceptron) error correction function is defined, with the help of the known recognition confusion calculation results, the actual input speech error is processed by gain mismatch, and the software execution environment of the system is built. Combined with the related hardware structure, the intelligent correction system of students’ English pronunciation error based on speech recognition technology is successfully applied, and the comparative experiment is designed the practical application value of the system is highlighted.

Design of Chinese-English Wireless Simultaneous Interpretation System Based on Speech Recognition Technology

A Chinese-English wireless simultaneous interpretation system based on speech recognition technology is suggested to solve the problems of low translation accuracy and a high number of ambiguous terms in current Chinese-English simultaneous interpretation systems. The system’s general structure and hardware architecture are summarized. The chairman unit, representative unit, transliteration unit, and auditing unit are the four basic components of the simultaneous interpretation system. The CPU is the nRF24E1 hardware wireless radio frequency transceiver chip, while the chairman machine, representative machine, translator, and auditorium are all created separately. Speech recognition technology is used by the system software to create a speech recognition process that properly produces speech-related semantics. The input text is used as the search criteria, a manual interactive synchronous translation program is created, and the results for the optimum translation impact are trimmed. The experimental findings reveal that this system’s sentence translation accuracy rate is 0.9–1.0, and the number of ambiguous terms is minimal, which is an improvement on previous systems’ low translation accuracy.