Evaluation of real-time captioning by machine recognition with human support
Hironobu Takagi, Takashi Itoh, Kaoru Shinkawa · 2015
Verbal meetings are important at work, but employees who are deaf or hard of hearing (DHH) find it difficult to participate. Manual real-time captioning is a solution, but professional stenographers are too expensive for routine use. We are exploring the possibilities of real-time captioning that combines Automated Speech Recognition (ASR) and human capabilities, which can dramatically decrease these costs and thus improve the lives of DHH employees. We developed a flexible ASR-based real-time captioning tool that can be used by non-expert captioners to correct the recognized text in practical workplace situations. In this paper, we will report on our early results, focusing on accuracy and latency.