Projects

Projects

Current Projects

TransType2
- Computer-Assisted Translation
The aim of TT2 is to develop a Computer-Assisted Translation (CAT) system, which will help to meet the growing demand for high-quality translation. The innovative solution proposed by TT2 is to embed a data-driven machine translation engine with an interactive translation environment. In this way, the system combines the best of two paradigms: the CAT paradigm, in which the human translator ensures high-quality output; and the MT paradigm, in which the machine ensures significant productivity gains. Another innovative feature of TT2 is that it will have two input modalities: text and speech. Six different versions of the system will be developed for English, French, Spanish and German. To ensure that TT2 corresponds to the translators' needs, two professional translation agencies will evaluate successive prototypes.
Project partners: SEMA Group, Lehrstuhl für Informatik VI, Computer Science Department, RWTH Aachen - University of Technology, Instituto Tecnologico de Informatica, RALI Laboratory - University of Montreal, Celer Soluciones, Societe Gamma, Xerox Research Centre Europe

LC-STAR
(Lexica and Corpora for Speech-to-Speech Translation Technologies)
The objective of the LC-STAR is to improve human-to-human and man-machine communication in multilingual environments. The project aims to create lexica and corpora needed for speech-to-speech translation. Within LC-STAR, quasi industrial standards for those language resources will be established, lexica for 12 languages and text corpora for 3 languages will be created. A speech to speech translation demonstrator for the three languages English, Spanish and Catalan will be developed. The Lehrstuhl für Informatik VI will focus on the investigation of speech centered translation technologies focusing on requirements concerning language resources and the creation of lexica for speech recognition in German.
LC-STAR is supported by the European Union. Project partners are Siemens AG (Germany), IBM Deutschland Entwicklung GmbH (Germany), Universitat Politecnica de Catalunya (Spain), NSC - Natural Speech Communication Ltd. (Israel), and Nokia Corporation (Finland).

RWTH Project 'Image Retrieval in Medical Applications' (IRMA)
The RWTH IRMA project is a joint project of the Institute of Medical Informatics, the Department of Diagnostic Radiology, and Lehrstuhl für Informatik VI. The goal of this project is the realization of a content-based image retrieval system suited for use in daily medical routine.

Examples from the IRMA database

CORETEX

Nowadays commercial speech recognition systems work well for a very specific task and language. However, they are not able to adapt to new domains, acoustic environments and languages. The objectives of the CORETEX project are to develop generic speech recognition technology that works well for a wide range of tasks with essentially no exposure to task specific data and to develop methods for rapid porting to new domains and languages with limited, inaccurately or untranscribed training data. Another objective is to investigate techniques to produce an enriched symbolic speech transcription with extra information for higher level (symbolic) processing and to explore methods to use contemporary and/or topic-related texts to improve language models, and for automatic pronunciation generation for vocabulary extension.
We began with first investigations in unsupervised training, i.e. train a speech recognition system for a new task without dedicated transcribed training data for this specific task. One problem with genericity and portability is the recognition vocabulary. When shifting to a new task, a lot of work has to be done to manually build phonetic transcriptions for new words. We developed a method for automatically determine the phonetic transcription (see section Pronunciation Modeling). Further we build a system to segment recorded broadcast shows into parts which can be handled by the speech recognition system.

Completed Projects

VERBMOBIL II

VERBMOBIL is a speaker-independent and bidirectional speech-to-speech translation system for spontaneous dialogues in mobile situations. It recognizes spoken input, analyses and translates it, and finally utters the translation. The multi-lingual system handles dialogues in three business-oriented domains, with context-sensitive translation between three languages (German, English, and Japanese).
Within the BMBF-funded project, the Lehrstuhl für Informatik VI performed research on both speech recognition and translation. For both tasks, statistical methods were used and self-contained software modules were developed and integrated into the final prototype system. For the speech recognition part we developed efficient search algorithms which perform a real time operation. In the end-to-end evaluation, the statistical machine translation significantly outperformed the competing translation approaches such as classical transfer-based translation or example-based translation.

EU-Project EuTrans

Machine translation has been receiving considerable attention for a long time, because of its great industrial and social interest. The focus of the EUTRANS project was the development and evaluation of example-based translation techniques for text and speech input. Our institute contributed acoustic models for the recognition of Italian telephone speech and analyzed different statistical translation techniques. EUTRANS was supported by the European Union ESPRIT LTR (Long Term Research) programme. Project partners were the Universidad Politecnica de Valencia (Spain), Fundacione Ugo Bordoni (Italy), Zeres GmbH (Bochum), and Lehrstuhl für Informatik VI.
ADVISOR
ADVISOR, a digital tool box for content analysis and rapid retrieval of videos, is a research and development project being supported by the European Commission, with additional funding from the companies and organizations undertaking the work. ADVISOR aims at pushing the state-of-the-art in video annotation and retrieval technologies to the extent that formal information from videos are extracted (semi) automatically. A digital tool box for content analysis and rapid retrieval of videos brings together audio and video indexing, manual annotation and a search engine. In this project, our institute provides an automatic segmentation algorithm for MPEG audio streams and a large vocabulary continuous real-time speech recognition system for transcribing the speech segments. The recognizer output is enriched with confidence measures for information retrieval purposes.

GIZA++
The aim of this project was the development of a publically available baseline toolkit for statistical machine translation. We extended an existing simple toolkit (GIZA) by implementing training algorithms for statistical translation models . This project was supported by the National Science Foundation (NSF).

Last modified November 2, 2001