8th International Conference on Spoken Language Processing

Jeju Island, Korea
October 4-8, 2004

Improved Spoken Language Translation Using N-best Speech Recognition Hypotheses

Ruiqiang Zhang, Genichiro Kikui, Hirofumi Yamamoto, Frank K. Soong, Taro Watanabe, Eiichiro Sumita, Wai-Kit Lo

ATR, Japan

We intended to demonstrate the effect of using N-best speech recognition hypotheses for improving speech translation performance. A log-linear model, which integrated features from speech recognition and statistical machine translation, was used to rescore the translation candidates. Model parameters were estimated by optimizing an objectively measurable but subjectively relevant translation quality metric. Experimental results have shown that the proposed N-best approach improved translation quality over the conventional single-best approach. The improvements were confirmed consistently by several automatic translation evaluation metrics.

Full Paper

Bibliographic reference.  Zhang, Ruiqiang / Kikui, Genichiro / Yamamoto, Hirofumi / Soong, Frank K. / Watanabe, Taro / Sumita, Eiichiro / Lo, Wai-Kit (2004): "Improved spoken language translation using n-best speech recognition hypotheses", In INTERSPEECH-2004, 1629-1632.