1. A method comprising: at a first time: receiving, from a first classifier, a first modality output detected from a voice input from a human subject; and receiving, from a second classifier, a second modality output detected from visual input from the human subject, wherein the voice input and the visual input are provided by the human subject at a same time, and wherein a mix and a weighting for the first classifier and the second classifier is based on speaking patterns for a given time of day for the human subject; providing, via a feedback loop, the first modality output to the second classifier, to yield an updated second classifier, wherein the first classifier and the updated second classifier comprise a classifier group; at a second time, after the first time: receiving classifier group output from the classifier group; concatenating, via a third classifier, the classifier group with original features of the human subject, to yield a classifier output, wherein the original features of the human subject comprise a temporal alignment of voice data associated with the first modality output and visual data associated with the second modality output; and determining voice activity based on the classifier output.