This paper describes a collaboration between Bell Labs and NHK (Japan Broadcasting Corp.) STRL to develop a real-time large vocabulary speech recognition system for live closed-captioning of NHK news programs. Bell Labs broadcast news recognition engine consists of a two-pass decoder using bigram language models (LM) and right biphone models during the first pass, and trigram LM with within-word triphone models in the second pass. Various pruning strategies are used to achieve real time decoding, together with a noise compensation procedure aimed at improving recognition on noisy segments of the program. The system operates in a real-time mode and delivers less than 2% of word error rate (WER) on studio news conditions and about 5% of WER on noisy news and reporter speech when evaluated on a real broadcast news program.
Cite as: Siohan, O., Ando, A., Afify, M., Jiang, H., Lee, C.-H., Li, Q., Liu, F., Onoe, K., Soong, F.K., Zhou, Q. (2001) A real-time Japanese broadcast news closed-captioning system. Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001), 495-498, doi: 10.21437/Eurospeech.2001-130
@inproceedings{siohan01_eurospeech,
author={Olivier Siohan and Akio Ando and Mohamed Afify and Hui Jiang and Chin-Hui Lee and Qi Li and Feng Liu and Kazuo Onoe and Frank K. Soong and Qiru Zhou},
title={{A real-time Japanese broadcast news closed-captioning system}},
year=2001,
booktitle={Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001)},
pages={495--498},
doi={10.21437/Eurospeech.2001-130}
}