Exploring Practical Metrics to Support Automatic Speech Recognition Evaluations.

Recent studies into the evaluation of automatic speech recognition for its quality of output in the form of text have shown that using word error rate to see how many mistakes exist in English does not necessarily help the developer of automatic transcriptions or captions. Confidence levels as to the type of errors being made remain low because mistranslations from speech to text are not always captured with a note that details the reason for the error. There have been situations in higher education where students requiring captions and transcriptions have found that some academic lecture results are littered with word errors which means that comprehension levels drop and those with cognitive, physical and sensory disabilities are particularly affected.

AI Supporting AAC Pictographic Symbol Adaptations.

The phenomenal increase in technological capabilities that allow the design and training of systems to cope with the complexities of natural language and visual representation in order to develop other formats is remarkable. It has made it possible to make use of image to image and text to image technologies to support those with disabilities in ways not previously explored. It has opened the world of adaptations from one picture to another in a design style of a user's choosing.

Co(0.85)Se/Multi-Walled Carbon Nanotube Composite as Alternative Cathode Catalyst for Microbial Fuel Cells.

The development of low-cost and high-performance cathode catalyst remains a great challenge for constructing large-scale microbial fuel cells (MFCs). Here, the multi-walled carbon nanotube (MWCNT) supported cobalt selenide (Co0.85Se) composite was synthesized by a simple and effective hydrothermal process for the first time.