Multi-View Learning of Acoustic Features for Speaker Recognition

We consider learning acoustic feature transformations using an additional view of the data, in this case video of the speaker's face. Specifically, we consider a scenario in which clean audio and video is available at training time, while at test time only noisy audio is available. We use canonical correlation analysis (CCA) to learn linear projections of the acoustic observations that have maximum correlation with the video frames. We provide an initial demonstration of the approach on a speaker recognition task using data from the VidTIMIT corpus. The projected features, in combination with baseline MFCCs, outperform the baseline recognizer in noisy conditions. The techniques we present are quite general, although here we apply them to the case of a specific speaker recognition task. This is the first work of which we are aware in which multiple views are used to learn an acoustic feature projection at training time, while using only the acoustics at test time.