Speech perception is complex and demands constant adaptations to the speaker and the environment (i.e. noisy speech, accent, etc.). To adapt, the listener relies on one speech feature more than another. This cognitive mechanism is called selective attention. We present a model that captures the idea of selective attention: we show that this dynamic adaptation process can be captured in a neural architecture by using a multiple encoder beta variational auto encoder (beta-ME-VAE), which is based on rate distortion theory. This model implements the idea that optimal feature weighting looks different under different listening conditions and provides insight into how listeners can adapt their listening strategy on a moment-to-moment basis, even in listening situations they haven't experienced before.