Fragile error handling in recognition-based systems is a major problem that degrades their performance, frustrates users, and limits commercial potential. The aim of the present research was to analyze the types and magnitude of linguistic adaptation that occur during spoken and multimodal human-computer error resolution. A semiautomatic simulation method with a novel error-generation capability was used to collect samples of users' spoken and pen-based input immediately before and after recognition errors, and at different spiral depths in terms of the number of repetitions needed to resolve an error. When correcting persistent recognition errors, results revealed that users adapt their speech and language in three qualitatively different ways. First, they increase linguistic contrast through altemation of input modes and lexical content over repeated correction attempts. Second, when correcting with verbatim speech, they increase hyperarticulation by lengthening speech segments and pauses, and increasing the use of final falling contours. Third, when they hyperarticulate, users simultaneously suppress linguistic variability in their speech signal's amplitude and fundamental frequency. These findings are discussed fVom the perspective of enhancement of linguistic intelligibility. Implications are also discussed for corroboration and generalization of the Computer-elicited Hyperarticulate Adaptation Model (CHAM), and for improved error handling capabilities in next-generation spoken language and multimodal systems. Acknowledgments: This research was supported by Grant No. IRl-9530666 from the National Science Foundation, and by grants, contracts, and equipment donations from Apple, GTE Labs, Intel, Microsoft, NTT Data, Southwestern Bell, and US. West. We especially thank Robert vanGent, Jon Lindsay, and Eric Iverson for adapting the simulation software to support these studies, Robert and Eric for acting as simulation assistants during data collection, and Robert, Jon, Karen Kuhn, and Yetunde Laniran for assistance with transcription, scoring, and preparation of figures. Thanks to Mark Fanty, Ed Kaiser, Terri Lander, Pieter Vermeulen, Karen Ward, and Lodewyk Wessels of CSLU for discussions and assistance with OGI's Speech Toolkit. Thanks also to Phil Cohen for helpful comments on an earlier manuscript draft. Finally, we are grateful to the people who generously volunteered their time to participate in this research.

Exponential increases in complexity and scale make variability a growing threat to sustaining HPC performance at exascale. Performance variability in HPC I/O is common, acute, and formidable. We take the first step towards comprehensively studying linear and nonlinear approaches to modeling HPC I/O system variability. We create a modeling and analysis approach (MOANA) that predicts HPC I/O variability for thousands of software and hardware configurations on highly parallel shared-memory systems. Our findings indicate nonlinear approaches to I/O variability prediction are an order of magnitude more accurate than linear regression techniques. We demonstrate the use of MOANA to accurately predict the confidence intervals of unmeasured I/O system configurations for a given number of repeat runs-enabling users to quantitatively balance experiment duration with statistical confidence.

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