About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
INTERSPEECH - Eurospeech 2003
Conference paper
Improving statistical natural concept generation in interlingua-based speech-to-speech translation
Abstract
Natural concept generation is critical to statistical interlinguabased speech translation performance. To improve maximumentropy- based concept generation, a set of novel features and algorithms are proposed including features enabling model training on parallel corpora, employment of confidence thresholds and multiple sets of features. The concept generation error rate is reduced by 43%-50% in our speech translation corpus within limited domains. Improvements are also achieved in our experiments on speech-to-speech translation.