The Development of Automatic Speech Recognition Software for Portable Devices

2008 ◽  
Author(s):  
Myoung-Wan Koo ◽  
Joon-Ki Choi ◽  
Young-Myoung Kim
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Portable Devices ◽  
Speech Recognition Software ◽  
Recognition Software

Implementation of a Voice-ControlSystem for Issuing Commands in a Virtual Manufacturing Simulation Process

2014 ◽  
Vol 980 ◽  
pp. 165-171
Author(s):  
Yun Suen Pai ◽  
Hwa Jen Yap ◽  
Ramesh Singh
Keyword(s):  
Speech Recognition ◽  
Application Programming Interface ◽  
Virtual Manufacturing ◽  
Numerical Control ◽  
Robot Arm ◽  
Cnc Machine ◽  
Simulation Process ◽  
Speech Recognition Software ◽  
Programming Knowledge ◽  
Recognition Software

Speech recognition is a technology that attempts to involve audio cues during interaction with machines, instead of being limited to just visual and touch interfaces. However, a keyboard and mouse input is an archaic method of interaction, adding on to the fact that voice control is seemingly more natural. This study aims to implement speech recognition as a form of machine control to perform simple commands in a virtual simulation process. The simulation system is an in-house developed augmented reality robotic work cell which includes a robot arm, a conveyer belt, a computer numerical control (CNC) machine, and a pellet. Issuing commands are performed via the Windows Speech Recognition software built from the Microsoft Speech Application Programming Interface (SAPI). This software is advantageous because it can be fairly accurate once trained properly, is easily modifiable by anyone regardless of the operator’s programming knowledge, and is free. A macros tool is used to support the additional features of the recognition software which includes directly programmable Extensible Markup Language (XML) codes.

Support Technique for Real-Time Captionist to Use Speech Recognition Software

2008 ◽  
pp. 647-650 ◽  
Author(s):  
Shigeki Miyoshi ◽  
Hayato Kuroki ◽  
Sumihiro Kawano ◽  
Mayumi Shirasawa ◽  
Yasushi Ishihara ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Speech Recognition Software ◽  
Recognition Software

Voice/Speech Recognition Software

2013 ◽  
pp. 1005-1011
Author(s):  
Andrew Kitchenham ◽  
Doug Bowes
Keyword(s):  
Speech Recognition ◽  
Assistive Technology ◽  
Special Needs ◽  
Learning Process ◽  
Voice Recognition ◽  
Special Needs Students ◽  
Speech Recognition Software ◽  
The Disabled ◽  
Recognition Software

In this chapter, the authors discuss the promise of speech or voice recognition software and provide practical suggestions for the teacher or any stakeholder working with a disabled child. The authors begin the chapter with a brief overview of the legislation mandating the accommodation of special needs students in the classroom and discuss the implications of assistive technology. The authors then move on to an examination of the promise of the software. The authors end the chapter with practical ideas for implementation should the caregiver believe that voice recognition software will assist the disabled child in the learning process.

Smartphone speech-to-text applications for communication with profoundly deaf patients

2015 ◽  
Vol 130 (1) ◽  
pp. 104-106 ◽  
Author(s):  
F C Lyall ◽  
P J Clamp ◽  
D Hajioff
Keyword(s):  
Speech Recognition ◽  
Confidence Interval ◽  
Communication Aids ◽  
Speech Recognition Software ◽  
Feasible Alternative ◽  
Standard Set ◽  
The Mean ◽  
Recognition Software ◽  
Slow Speech ◽  
Mean Time

AbstractObjective:Visual communication aids, such as handwriting or typing, are often used to communicate with deaf patients in the clinic. This study aimed to establish the feasibility of communicating through smartphone speech recognition software compared with writing or typing.Method:Thirty doctors and medical students were timed writing, typing and dictating a standard set of six sentences appropriate for a post-operative consultation, and the results were assessed for accuracy and legibility.Results:The mean time for smartphone dictation (17.8 seconds, 95 per cent confidence interval = 17.0–18.7) was significantly faster than writing (59.2 seconds, 95 per cent confidence interval = 56.6–61.7) or typing (44 seconds, 95 per cent confidence interval = 41.0–47.1) (p< 0.001). Speech recognition was slightly less accurate, but accuracy increased with time spent dictating.Conclusion:Smartphone dictation is a feasible alternative to typing and handwriting. Slow speech may improve accuracy. Early clinical experience has been promising.

scholarly journals Comparing the impact of automatically generated and corrected subtitles on cognitive load and learning in a first- and second-language educational context

2020 ◽  
Vol 18 ◽  
Author(s):  
Wing Shan Chan ◽  
Jan-Louis Kruger ◽  
Stephen Doherty
Keyword(s):  
Second Language ◽  
Speech Recognition ◽  
Recognition Accuracy ◽  
Error Rates ◽  
Future Studies ◽  
Speech Recognition Software ◽  
Video Lectures ◽  
Second Language Speakers ◽  
Recognition Software ◽  
The Impact

The addition of subtitles to videos has the potential to benefit students across the globe in a context where online video lectures have become a major channel for learning, particularly because, for many, language poses a barrier to learning. Automated subtitling, created with the use of speech-recognition software, may be a powerful way to make this a scalable and affordable solution. However, in the absence of thorough post-editing by human subtitlers, this mode of subtitling often results in serious errors that arise from problems with speech recognition, accuracy, segmentation and presentation speed. This study therefore aims to investigate the impact of automated subtitling on student learning in a sample of English first- and second-language speakers. Our results show that high error rates and high presentation speeds reduce the potential benefit of subtitles. These findings provide an important foundation for future studies on the use of subtitles in education.

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