This paper describes a speaker activity detector taking co-channel speech as input and labeling intervals of the input as target-only, jammer-only, or two-speaker (target+jammer). The algorithms applied were borrowed primarily from speaker recognition, thereby allowing us to use speaker-dependent test-utterance-independent information in a front-end for co-channel talker interference suppression. Parameters studied included classifier choice (vector quantization vs. Gaussian), training method (unsupervised vs. supervised), test utterance segmentation (uniform vs. adaptive), and training and testing target-to-jammer ratios. Using analysis interval lengths of 100 ms, performance reached 80% correct detection.