This is a basic research project whose thrust is both theoretical and practical in nature. The core technology consists of techniques using machine learning and statistical theory as well as fundamental linguistic and phonetic theory. The investigations aim at furthering the understanding of requirements for future speech recognition systems, and in developing strategies for extracting significant information from noisy and/or large quantities of language data. Both the theoretical and

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... sides of the research have demonstrated advances. Phonetic regularities have been discovered; phonetic processing architectures and parameter tracking methods, and the CDT algorithm have been developed, all of which take into account contextual factors associated with phonetic variation. Along with progress in variation, a distance metric has been developed for co-channel speech-interference. Finally, progress has been furthered in understanding information extraction in unrestricted language data, and part-of-speech annotation has been demonstrated. The most recent accomplishments include: