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The analysis of spoken language is widely considered to be a more challenging task than the analysis of written text. All of the difficulties of written language can generally be found in spoken language as well. Parsing spontaneous speech must, however, also deal with problems such as speech disfluencies, the looser notion of grammaticality, and the lack of clearly marked sentence boundaries. The contamination of the input with errors of a speech recognizer can further exacerbate these problems. Most natural language parsing algorithms are designed to analyze "clean" grammatical input. Because they reject any input which is found to be ungrammatical in even the slightest way, such parsers are unsuitable for parsing spontaneous speech, where completely grammatical input is the exception more than the rule. This thesis describes GLR*, a parsing system based on Tomita's Generalized LR parsing algorithm, that was designed to be robust to two particular types of extra-grammaticality: noise...

A simple and general method is described that can combine different knowledge sources to reorder N-best lists of hypothe-ses produced by a speech recognizer. The method is automat-ically trainable, acquiring information from both positive and negative examples. In experiments, the method was tested on a 1000-utterance sample of unseen ATIS data.

We describe how substantial domainindependent language-processing systems for French and Spanish were quickly developed by manually adapting an existing English-language system, the SRI Core Language Engine. We explain the adaptation process in detail, and argue that it provides a fairly general recipe for converting a grammar-based system for English into a corresponding one for a Romance language.

The paper presents an overview of the Spoken Language Translator (SLT) system's hybrid language-processing architecture, focussing on the way in which rule-based and statistical methods are combined to achieve robust and efficient performance within a linguistically motivated framework. In general, we argue that rules are desirable in order to encode domain-independent linguistic constraints and achieve high-quality grammatical output, while corpus -derived statistics are needed if systems are to be efficient and robust; further, that hybrid architectures are superior from the point of view of portability to architectures which only make use of one type of information. We address the topics of "multi-engine" strategies for robust translation; robust bottom-up parsing using pruning and grammar specialization; rational development of linguistic rule-sets using balanced domain corpora; and efficient supervised training by interactive disambiguation. All work described is fully implemented...

JANUS is a multi-lingual speech-to-speech translation system, which has been designed to translate spontaneous spoken language in a limited domain. In this paper, we describe our recent preliminary efforts to expand the domain of coverage of the system from the rather limited Appointment Scheduling domain, to the much richer Travel Planning domain. We compare the two domains in terms of out-of-vocabulary rates and linguistic complexity. We discuss the challenges that these differences impose on our translation system and some planned changes in the design of the system. Initial evaluations on Travel Planning data are also presented.

Systems now exist which are able to compile unification grammars into language models that can be included in a speech recognizer, but it is so far unclear whether non-trivial linguistically principled grammars can be used for this purpose. We describe a series of experiments which investigate the question empirically,by incrementally constructing a grammar and discovering what problems emerge when successively larger versions are compiled into finite state graph representations and used as language models for a medium-vocabulary recognition task.

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We report results from a cross-language study of disfluencies (DFs) in Swedish and American English humanmachine and human-human dialogs. The focus is on comparisons not directly affected by differences in overall rates since these could be associated with task details. Rather, we focus on differences suggestive of how speakers utilize DFs in the different languages, including: relative rates of the use of hesitation forms, the location of hesitations, and surface characteristics of DFs. Results suggest that although the languages differ in some respects (such as the ability to insert filled pauses within `words'), in many analyses the languages show similar behavior. Such results provide suggestions for cross-linguistic DF modeling in both theoretical and applied fields. 1. INTRODUCTION In recent years, automatic speech recognition (ASR) systems have attained accuracy levels on constrained tasks that are sufficient for many commercial purposes. However for more open-ended speech inp...

Most spoken language translation systems developed to date rely on a pipelined architecture, in which the main stages are speech recognition, linguistic analysis, transfer, generation and speech synthesis. When making projections of error rates for systems of this kind, it is natural to assume that the error rates for the individual components are independent, making the system accuracy the product of the component accuracies. The paper reports experiments carried out using the SRI-SICSTelia Research Spoken Language Translator and a 1000-utterance sample of unseen data. The results suggest that the naive performance model leads to serious overestimates of system error rates, since there are in fact strong dependencies between the components. Predicting the system error rate on the independence assumption by simple multiplication resulted in a 16% proportional overestimate for all utterances,