Linguistic processing in spoken dialogue systems has to be robust against a large number of phenomena such as recognizer errors, spontaneous speech phenomena and out-of-vocabulary (OOV) words. A commonly used solution to this problem is partial parsing, that aims at detecting only parts of sentences/utterances that are vital for the respective task of the parser. In our paper we present a framework for robust linguistic processing in our spoken dialogue system EVAR for train timetable information. The linguistic processor combines partial parsing with prosody and statistical concept prediction. Parsing is restricted to the detection and analysis of those parts of an utterance that are crucial for its understanding by the system. In order to accomplish this task most efficiently, the parser operates not only on word lattices as delivered by the recognizer, but also on prosodic information and statistical concept prediction.
Cite as: Nöth, E., Haas, J., Warnke, V., Gallwitz, F., Boros, M. (1999) A hybrid approach to spoken dialogue understanding: prosody, statistics and partial parsing. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 2019-2022, doi: 10.21437/Eurospeech.1999-446
@inproceedings{noth99_eurospeech,
author={Elmar Nöth and Jürgen Haas and Volker Warnke and Florian Gallwitz and Manuela Boros},
title={{A hybrid approach to spoken dialogue understanding: prosody, statistics and partial parsing}},
year=1999,
booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
pages={2019--2022},
doi={10.21437/Eurospeech.1999-446}
}