Minimum Bayes risk signal detection for speech enhancement based on a narrowband DOA model

A desired speech signal in hands-free communication systems is often degraded by background noise and interferers. Data-dependent spatial filters for desired speech extraction depend on the power spectral density (PSD) matrices of the desired and the undesired signals, which are commonly estimated recursively using a signal model-based speech presence probability (SPP). The SPP and the PSD matrix estimates are only accurate, if the statistics of the undesired signals vary more slowly compared to the desired signal. In practical situations with competing talkers, this assumption is violated. To estimate the PSD matrices of highly non-stationary signals, we propose a minimum Bayes risk detector based on a model for the narrowband direction-of-arrival estimates. The performance of the proposed detector and the objective quality of the extracted desired speech are evaluated using simulated and measured data.