In this paper we present a two-stage best-first bottom-up word-lattice parser which we use as a language model for speech recognition. The parser works by using a "Figure of Merit" that selects lattice paths while simultaneously selecting syntactic category edges for parsing. Additionally, we introduce a modified version of the Inside-Outside algorithm used as a pruning stage between syntactic context-free parsing and lexicalized context-dependent parsing. We report our results in terms of Word Error Rate on the HUB--1 word-lattices and compare these results to other syntactic language modeling techniques.

The paper is concerning an approach for understanding speech using a new form of probabilistic models to represent syntactic and semantic knowledge of a restricted domain. One important feature of our grammar is that the parse tree directly represents the semantic content of the utterance. Since we determine that semantic content by an integrated search, we avoid consistency problems at the interface between the recognizer and the language understanding part of the speech understanding system. We succeeded in designing such an incremental algorithm, which integrates semantic, syntactic, and acoustic-phonetic knowledge in a seamless, consistent way. High efficiency is achieved by using a chart-parsing technique with structuresharing and a strict top-down strategy for opening new word hypotheses in the pronunciation layer.

This paper revisits word lattice search whose task is to find a plausible semantic interpretation for a given utterance. Our approach of interleaved search and analysis is designed to break the frontier of "toy" applications. The framework is implemented in two interacting modules, running in parallel. Instead of simply parsing a word lattice, we rather do tree decoding with a probabilistic approximation of a given grammar, employing a beam search strategy. Logical form is build up in tandem according to the decoded derivation histories, using a codescriptive HPSG grammar for dialog turns. The proposed architecture only uses the knowledge necessary in every processing step, the key aspect being an asynchronous coupling of the two specialized modules.

. This article describes a probabilistic context free grammar approximation method for unification grammars. In order to produce good results, the method is combined with an N best parsing extension to chart parsing. The first part of the paper introduces the grammar approximation method, while the second part describes details of an efficient N-best packing and unpacking scheme for chart parsing. 1 Introduction Recently much attention has been payed to the integration of speech and language technology 1 . The concentration on spontaneous speech understanding led to the definition of a robust interface known as the word graph or word lattice between recognition and understanding. Depending on the application, systems are built to provide a shallow stochastic analysis or a deep linguistic analysis of the word lattice. Using a shallow stochastic approach, a rough template-based analysis can be achieved which makes sense in those cases where a fine grained reconstruction of meanings is...

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This report presents some word recognition results on the speech-to-speech translation system INTARC 2.0. Our research goal was to build a system with a cognitive oriented architecture. The main topics are incremental, timesynchronous and interactive processing. All modules work on the same time segment processing the signal from left to right. Analyses -- even partial ones -- are passed as soon as possible. Figure 1 shows the overall structure of the system. Details of the architecture of parts of INTARC 2.0 can be found in [1]...

The German joint research project Verbmobil (VM) aims at the development of a speech to speech translation system. This paper reports on research done in our group which belongs to Verbmobil’s subproject on system architectures (TP15). Our specific research areas are the construction of parsers for spontaneous speech, investigations in the parallelization of parsing and to contribute to the development of a flexible communication architecture with distributed control.