Extending Limabeam with discrimination and coarse gradients

Limabeam is an approach to multi-microphone array processing for ASR which makes minimal assumptions about system geometry, instead searching for filters to maximise output likelihoods under a speech model. The first results of Limabeam on the AMI meeting corpus are given, then two extensions of the algorithm for this corpus. First, it is shown that the original local gradient following sticks in local minima, and a coarser gradient is used. Second, a new discriminative objective function is provided to handle mis-matched silence models. The extensions are based on examination of 2D receptive fields and 2D likelihood maps which are novel near-field analogs of radial beamformer response patterns, but do not show radial symmetry and have many local minima. The extended Limabeam improves WER on TDOA baselines on the AMI corpus, by 1% rel. when both are adapted with decodes and by 19% rel. when both adapted with ground truth.