A lot of work remains to be done in the domain of a better integration of speech recognition and language processing systems. This paper gives an overview of several strategies for integrating linguistic models into speech understanding systems and investigates several ways of producing sets of hypotheses that include more "semantic" variability than usual language models. The main goal is to present and demonstrate by actual experiments that sequential coupling may be efficiently achieved by word-lattice syntactic analyzers, efficiently parsing the huge number of hypothesis (i.e. possible sentences) contained in the lattice produced by the speech recognizer. 1. Motivations The past decade has seen significant progress in speech recognition technology: word (recognition) error rates continue to drop by a factor of 2 every two years (Rabiner et al., 1996) and high performance systems are now becoming available. Several factors have contributed to this rapid progress: ffl Generalisati...

The adaptation of existing general-purpose speech recognition and language understanding systems can greatly reduce the cost of developing applications. However, the components must have appropriate characteristics for this to be possible. Work is in progress to adapt two task-independent components, the AURIX speech recognizer and the CLARE language processor to create a system allowing spoken queries of the PC-based Autoroute route planning package. Keywords: adaptability, general purpose, speech recognition, language understanding, AURIX, CLARE 1. INTRODUCTION A spoken language understanding system is being built by the reconfiguration of two general purpose components. AURIX is designed to be a reconfigurable speech recognizer generating either a string or words or a lattice. Either input can be fed into CLARE, a general purpose language processor, which can generate suitable commands or database queries for a particular application. In the following sections, we describe first...

We have recently developed a statistical model of speech that avoids a number of current constraining assumptions for statistical speech recognition systems, particularly the model of speech as a sequence of stationary segments consisting of uncorrelated acoustic vectors. We further wish to focus statistical modeling power on perceptually-dominant and information-rich portions of the speech signal, which may also be the parts of the speech signal with a better chance to withstand adverse acoustical conditions. We de- scribe here some of the theory, along with some preliminary experiments. These experiments suggest that the regions of acoustic signal containing significant spectral change are critical to the recognition of continuous speech.