Abstract

We first describe EVAR, a state--of--the--art speech understanding and dialog system which application domain is train time table information retrieval. The system --- as others --- is devided into two main components: a speech recognizer based on hidden Markov models (a statistical approach) and a knowledgebased linguistic component developed in a semantic network environment. When switching to a new domain the knowledge--base would have to be changed manually, which means a great effort. Thus, we favor for a statistical approach for linguistic analysis. This would only mean retraining the models on annotated corpora from the new domain. A promising approach are semantic classification trees. Since the speech recognizer already relies on statistical models only (part of) its parameters have to be retrained when switching to a new domain. 1 Introduction Automatic speech recognition (ASR) and automatic speech understanding (ASU) are difficult tasks even for read speech. In ASR one has...

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