| Paper: | SS-1.6 | ||
| Session: | Multilinguality in Speech Processing | ||
| Time: | Tuesday, May 18, 14:25 - 14:42 | ||
| Presentation: | Special Session Lecture | ||
| Topic: | Special Sessions: Multilinguality in Speech Processing | ||
| Title: | TOWARDS LANGUAGE PORTABILITY IN STATISTICAL SPEECH TRANSLATION | ||
| Authors: | Alex Waibel; Carnegie Mellon University | ||
| Tanja Schultz; Carnegie Mellon University | |||
| Stephan Vogel; Carnegie Mellon University | |||
| Christian Fügen; Karlsruhe University | |||
| Matthias Honal; Karlsruhe University | |||
| Muntsin Kolss; Karlsruhe University | |||
| Jürgen Reichert; Karlsruhe University | |||
| Sebastian Stüker; Karlsruhe University | |||
| Abstract: | In this paper we presented an approach towards the tighter coupling of statically based speech translation that uses multiple layers of reduction and transformation by cascading several stochastic source-channel models. This approach more radically relies on learning techniques to overcome today’s limits of language and domain portable conversational speech translation systems. The disfluency cleaner for English achieved a recall of 77.2% and a precision of 90.2%. The same algorithms and models were effortless adapted to Mandarin Chinese giving 49.4% recall and 76.8% precision. The results on translation suggest that MT systems can be successfully constructed for any language pair by cascading multiple MT systems via English. Moreover, end-to-end performance can be improved, if the interlingua language is enriched with additional linguistic information that can be derived automatically and monolingually in a data-driven fashion. | ||
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