Speaker Linking and Applications using Non-Parametric Hashing Methods
Abstract : Large unstructured audio data sets have become ubiquitous and present a challenge for organization and search. One logical approach for structuring data is to find common speakers and link occurrences across different recordings. Prior approaches to this problem have focused on basic methodology for the linking task. In this paper, we introduce a novel trainable nonparametric hashing method for indexing large speaker recording data sets. This approach leads to tunable computational complexity methods for speaker linking. We focus on a scalable clustering method based on hashingcanopy-clustering. We apply this method to a large corpus of speaker recordings, demonstrate performance tradeoffs, and compare to other hashing methods.
We describe preliminary results from an effort to quantify the uncertainties in parton distribution functions and the resulting uncertainties in predicted physical quantities. The production cross section of the $W$ boson is given as a first example. Constraints due to the full data sets of the CTEQ global analysis are used in this study. Two complementary approaches, based on the Hessian and the Lagrange multiplier method respectively, are outlined. We discuss issues on obtaining meaningful uncertainty estimates that include the effect of correlated experimental systematic uncertainties and illustrate them with detailed calculations using one set of precision DIS data.