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...

A speech recognizer is a device that translates speech into text. Many current speech recognizers contain two components, an acoustic model and a statistical language model. The acoustic model indicates how likely it is that a certain word corresponds to a part of the acoustic signal (e.g. the speech). The statistical language model indicates how likely it is that a certain word will be spoken next, given the words recognized so far. Even though the acoustic model might for example not be able to decide between the acoustically similar words "peach" and "teach", the statistical language model can indicate that the word "peach" is more likely if the previously recognized words are "He ate the". Current speech recognizers perform well on constrained tasks, but the goal of continuous, speaker independent speech recognition in potentially noisy environments with a very large vocabulary has not been reached so far. How can statistical language models be improved so that more complex tasks c...

this paper, we are essentially Interested in the language model Implemented in the linguistic component, and we leave aside the acoustic module. More precisely, we aim at Improving this linguistic model by coupling the ADS with a syntactic parser, able to diagnose and correct grammatical errors. We describe the characteristics of such a coupling, and show how the performance of the ADS improves with the actual coupling realized for French between the Tangore ADS and the grammar checker developed at the IBM France Scientific Center

This paperintroduces a simple mixtare language model that attempts to capture long distance conslraints in a sentence or paragraph. The model is an m-component mixture of Irigram models. The models were constructed using a 5K vocabulary and trained using a 76 mil-lion word Wail Street Journal text corpus. Using the BU recognition system, experiments show a 7 % improvement in recognition accu-racy with the mixture trigram models as compared to using a Irigram model. 1.