This thesis is about modeling, analyzing, and predicting errorful behavior in large vocabulary continuous speech recognition systems. Because today's state-of-the-art recognizers are not designed to be situated naturally in an error feedback loop, they are ill-positioned for inclusion in multi-modal interfaces, multi-media databases, and other interesting applications. I make improvements to the current approach to predicting and analyzing error behaviors, which is currently based only on the measurement ofword error rate. The speech recognizer's functionality is extended to include con dence annotations, which are \meta-level " markings that indicate how certain the recognizer is that it has decoded its input correctly. This is accomplished by feeding externally de ned error conditions back to the recognizer. Error feedback enables the construction of statistical models that map measurements of the recognizer's internal states and behaviors to externally de ned error conditions.

This paper provides an overview of the state-of-the-art in laboratory speaker-independent, large vocabulary continuous speech recognition (LVCSR) systems with a view towards adapting such technology to the requirements of real-world applications. While in speech recognition the principal concern is to transcribe the speech signal as a sequence of words, the same core technology can be applied to domains other than dictation. The main topics addressed are acoustic-phonetic modeling, lexical representation, language modeling, decoding and model adaptation. After a brief summary of experimental results some directions towards usable systems are given. In moving from laboratory systems towards real-world applications, different constraints arise which influence the system design. The application imposes limitations on computational resources, constraints on signal capture, requirements for noise and channel compensation, and rejection capability. The difficulties and costs of adapting existing technology to new languages and application need to be assessed. Near term applications for LVCSR technology are likely to grow in somewhat limited domains such as spoken language systems for information retrieval, and limited domain dictation. Perspectives on some unresolved problems are given, indicating areas for future research

The goal of this study is to evaluate the potential for using large vocabulary continuous speech recognition as an engine for automatically classifying utterances according to the language being spoken. The problem of language identification is often thought of as being separate from the problem of speech recognition. But in this paper, as in Dragon's earlier work on topic and speaker identification, we explore a unifying approach to all three message classification problems based on the underlying stochastic process which gives rise to speech. We discuss the theoretical framework upon which our message classification systems are built and report on a series of experiments in which this theory is tested, using large vocabulary continuous speech recognition to distinguish English from Spanish. 1.

This document contains draft information from a section of a preliminary version of a VERBMOBIL deliverable (TP 5.3-P1). It is distributed in this form to assist partners in advance planning. This document contains a simple version of the core DATR inference engine in Prolog in order to illustrate the principles of DATR inference to Prolog programmers. Note that in minor details it departs slightly from DATR conventions: - nonstandard nodenames are permitted; - the knowledge base must be pre-sorted to permit 'longest path first' inference; - queries include the theory name. Note also that this is not a directly usable implementation: there is no user interface, no DATR-Prolog interpreter, no DATR-specific trace or debugging, no attention paide to efficiency, etc. The aim is to provide a minimal 'core DATR standard inference' interpreter in logical style. 1 Illustration of a DATR theory: a 'microlexicon' MINILEX.DTR Tablecloth: !? == Compound !ilex? == lemma !relation? == (for covering) !modifier? == "Table:!?" !head? == "Cloth:!?". Table: !? == Simplex !ilex? == lemma !meaning? == (horizontal surface to put things on) !orthography? == (t a b l e). Cloth: !? == Simplex !ilex? == lemma 32 Dafydd Gibbon !meaning? == (variety of textile) !orthography? == (c l o t h). Compound: !? == Word !ilex? == generalisation !type? == compound !meaning? == ("!head meaning?" "!relation?" "!modifier meaning?") !orthography? == ("!modifier orthography?" "!head orthography?"). Simplex: !? == Word !ilex? == generalisation !type? == simplex. Word: !ilex? == generalisation !type? == word. Theorems: Tablecloth:!relation?=(for covering). Tablecloth:!meaning?=(variety of textile for covering horizontal surface to put things on). Tablecloth:!orthography?=(t a b l e c l o t h). Table:!orthography...