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

In-ear EEG biometrics for feasible and readily collectable real-world person authentication

The use of EEG as a biometrics modality has been investigated for about a decade, however its feasibility in real-world applications is not yet conclusively established, mainly due to the issues with collectability and reproducibility. To this end, we propose a readily deployable EEG biometrics system based on a `one-fits-all' viscoelastic generic in-ear EEG sensor (collectability), which does not require skilled assistance or cumbersome preparation. Unlike most existing studies, we consider data recorded over multiple recording days and for multiple subjects (reproducibility) while, for rigour, the training and test segments are not taken from the same recording days. A robust approach is considered based on the resting state with eyes closed paradigm, the use of both parametric (autoregressive model) and non-parametric (spectral) features, and supported by simple and fast cosine distance, linear discriminant analysis and support vector machine classifiers. Both the verification and identification forensics scenarios are considered and the achieved results are on par with the studies based on impractical on-scalp recordings. Comprehensive analysis over a number of subjects, setups, and analysis features demonstrates the feasibility of the proposed ear-EEG biometrics, and its potential in resolving the critical collectability, robustness, and reproducibility issues associated with current EEG biometrics

Have We Observed the Higgs (Imposter)?

We interpret the new particle at the Large Hadron Collider as a CP-even scalar and investigate its electroweak quantum number. Assuming an unbroken custodial invariance as suggested by precision electroweak measurements, only four possibilities are allowed if the scalar decays to pairs of gauge bosons, as exemplified by a dilaton/radion, a non-dilatonic electroweak singlet scalar, an electroweak doublet scalar, and electroweak triplet scalars. We show that current LHC data already strongly disfavor both the dilatonic and non-dilatonic singlet imposters. On the other hand, a generic Higgs doublet give excellent fits to the measured event rates of the newly observed scalar resonance, while the Standard Model Higgs boson gives a slightly worse overall fit due to the lack signal in the tau tau channel. The triplet imposter exhibits some tension with the data. The global fit indicates the enhancement in the diphoton channel could be attributed to an enhanced partial decay width, while the production rates are consistent with the Standard Model expectations. We emphasize that more precise measurements of the ratio of event rates in the WW over ZZ channels, as well as the event rates in b bbar and tau tau channels, are needed to further distinguish the Higgs doublet from the triplet imposter.Comment: 20 pages, 4 figures; v2: updated with most recent public data as of August 7. A generic Higgs doublet now gives the best fit to data, while the triplet imposter exhibits some tensio