Large margin semi-tied covariance transforms for discriminative training

We discuss the applicability of large margin techniques to the problem of estimating linear transforms for discriminative training of a semi-tied covariance (STC) model. Since STC models are good proxies for full-covariance (FC) Gaussian models, the idea is to combine the benefit of the latest discriminative training techniques and the modeling advantage of FC Gaussians at a much lower computational cost. We study the interaction of these transforms with feature-space and model-space discriminative training on state-of-the-art speaker adapted systems built for a large-scale Arabic broadcast news transcription task.