Speech Recognition with a Seamlessly Updated Language Model for Real-Time Closed-Captioning

摘要

It is desirable to consistently and seamlessly update a language model of speech recognition without stopping it for online applications such as real-time closed-captioning. This paper proposes a novel speech recognition system that enables the model to be updated at any time even while it is running. It can run the second decoder with the latest model in parallel, and their priority that must be accessed is controlled at a non-speech portion by an additional job process, which sends acoustic features only to an active target decoder with the latest model and sends recognized words to the backend manual error correction for closed-captioning. The system seamlessly updates the model and ensures endless speech recognition with the latest model at any time. Our new practical real-time closed-captioning system reduced word errors by two thirds with the proposed language model update mechanism in the speech recognition and captioning experiments for Japanese broadcast news programs.