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...

In this paper we present a new approach of combining stochastic language models and traditional linguistic models to enhance the performance of our spontaneous speech recognizer. We compile arbitrary large linguistic context dependencies into a category based bigram model which allows us to use a standard beam-search driven forward Viterbi algorithm for real time decoding. Since this recognizer is used in a dialog system, the information about the last system utterance is used to build dialogstep dependent language models. This setup is verified and tested on our corpus of spontaneous speech utterances collected with our dialog system. Experimental results show a significant reduction of word error rate. 1. INTRODUCTION In the last years it has been shown that the consideration of language constraints is vital for effective and efficient speech recognition. Typically, these language constraints are modeled in a so called language model which will restrict the allowed seqences of words...

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...