ON THE IMPORTANCE OF DIFFERENT FREQUENCY BINS FOR SPEAKER VERIFICATION

The majority of modern speaker verification systems take spectral analysis-based features as input, which contains multiple frequency bins. Naturally, there would be a question of whether all different frequency bins contribute equally to the speaker verification system performance? In this paper, we propose the frequency reweighting layer (FRL) to automatically learn and balance the importance of different frequency bins. This new layer can be freely inserted into the original speaker embedding learner once or multiple times at different layers, with an ignorable number of new parameters. Based on the proposed novel architecture, a set of experiments are designed and carried out on the VoxCeleb1 dataset, which not only achieves superior performance but also exhibits an interesting weight distribution -- the lower frequencies matter more.