Gales, MJF and Liu, X and Sinha, R and Woodland, PC and Yu, K and Matsoukas, S and Ng, T and Nguyen, K and Nguyen, L and Gauvain, JL and Lamel, L and Messaoudi, A (2007) Speech recognition system combination for machine translation. In: UNSPECIFIED.Full text not available from this repository.
The majority of state-of-the-art speech recognition systems make use of system combination. The combination approaches adopted have traditionally been tuned to minimising Word Error Rates (WERs). In recent years there has been growing interest in taking the output from speech recognition systems in one language and translating it into another. This paper investigates the use of cross-site combination approaches in terms of both WER and impact on translation performance. In addition the stages involved in modifying the output from a Speech-to-Text (STT) system to be suitable for translation are described. Two source languages, Mandarin and Arabic, are recognised and then translated using a phrase-based statistical machine translation system into English. Performance of individual systems and cross-site combination using cross-adaptation and ROVER are given. Results show that the best STT combination scheme in terms of WER is not necessarily the most appropriate when translating speech. © 2007 IEEE.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||02 Sep 2016 17:06|
|Last Modified:||23 Oct 2016 22:46|