In this paper we formulate a problem which incorporates nominal 3c2 performance and robust k ' , performance in the presence of a scalar perturbation. It is shown that this problem can be solved via a sequence of finite dimensional quadratic programming problems.

In this paper, we address the problem of speaker verification in conditions unseen or unknown during development. A standard method for consists extracting embeddings with a deep neural network and processing them through backend composed probabilistic linear discriminant analysis (PLDA) global logistic regression score calibration. This is known to result systems that work poorly on different ...

A common goal in statistics and machine learning is to learn models that can perform well against distributional shifts, such as latent heterogeneous subpopulations, unknown covariate shifts or unmodeled temporal effects. We develop analyze a distributionally robust stochastic optimization (DRO) framework learns model providing good performance perturbations the data-generating distribution. gi...