Abstract / truncated to 115 words (read the full abstract)

The thesis deals with automatic interpretation of naturally spoken utterances for limited-domain applications. Specifically, the problem is examined by means of a dialogue system for an airport information application. In contrast to traditional two-stage systems, speech recognition and semantic processing are tightly coupled. This avoids interpretation errors due to early decisions. The presented one-stage decoding approach utilizes a uniform, stochastic knowledge representation based on weighted transition network hierarchies, which describe phonemes, words, word classes and semantic concepts. A robust semantic model, which is estimated by combination of data-driven and rule-based approaches, is part of this representation. The investigation of this hierarchical language model is the focus of this work. Furthermore, methods for modeling out-of-vocabulary words ... toggle 18 keywords

hierarchical language model – statistical language model – speech interpretation – speech understanding – speech recognition – spoken dialogue – one-stage decoding – robust semantic modeling – weighted transition network hierarchy – out-of-vocabulary words – oov words – semantic tree – tree matching – semantic tree node accuracy – uniform knowledge representation – natural speech – smoothing – n-gram

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