  This paper addresses the problem of Target Activity Detection (TAD) for
binaural listening devices. TAD denotes the problem of robustly detecting the
activity of a target speaker in a harsh acoustic environment, which comprises
interfering speakers and noise (cocktail party scenario). In previous work, it
has been shown that employing a Feed-forward Neural Network (FNN) for detecting
the target speaker activity is a promising approach to combine the advantage of
different TAD features (used as network inputs). In this contribution, we
exploit a larger context window for TAD and compare the performance of FNNs and
Recurrent Neural Networks (RNNs) with an explicit focus on small network
topologies as desirable for embedded acoustic signal processing systems. More
specifically, the investigations include a comparison between three different
types of RNNs, namely plain RNNs, Long Short-Term Memories, and Gated Recurrent
Units. The results indicate that all versions of RNNs outperform FNNs for the
task of TAD.
