Max-margin Metric Learning for Speaker Recognition

Probabilistic linear discriminant analysis (PLDA) is a popular normalization approach for the i-vector model, and has delivered state-of-the-art performance in speaker recognition. A potential problem of the PLDA model, however, is that it essentially assumes Gaussian distributions over speaker vectors, which is not always true in practice. Additionally, the objective function is not directly related to the goal of the task, e.g., discriminating true speakers and imposters. In this paper, we propose a max-margin metric learning approach to solve the problems. It learns a linear transform with a criterion that the margin between target and imposter trials are maximized. Experiments conducted on the SRE08 core test show that compared to PLDA, the new approach can obtain comparable or even better performance, though the scoring is simply a cosine computation

Parallel-cascade-based mechanisms for heating solar coronal loops: test against observations

The heating of solar coronal loops is at the center of the problem of coronal heating. Given that the origin of the fast solar wind has been tracked down to atmospheric layers with transition region or even chromospheric temperatures, it is worthy attempting to address whether the mechanisms proposed to provide the basal heating of the solar wind apply to coronal loops as well. We extend the loop studies based on a classical parallel-cascade scenario originally proposed in the solar wind context by considering the effects of loop expansion, and perform a parametric study to directly contrast the computed loop densities and electron temperatures with those measured by TRACE and YOHKOH/SXT. This comparison yields that with the wave amplitudes observationally constrained by SUMER measurements, while the computed loops may account for a significant fraction of SXT loops, they seem too hot when compared with TRACE loops. Lowering the wave amplitudes does not solve this discrepancy, introducing magnetic twist will make the comparison even less desirable. We conclude that the nanoflare heating scenario better explains ultraviolet loops, while turbulence-based steady heating mechanisms may be at work in heating a fraction of soft X-ray loops.Comment: 6 pages, 1 figure, to appear in proceedings of AstroNum-201