GuidedMix: An on‐the‐fly data augmentation approach for robust speaker recognition system

Abstract

Abstract Data augmentation is an essential technique for building a high‐robustness speaker recognition system. this letter proposes a novel on‐the‐fly data augmentation strategy called GuidedMix. It significantly increases augmented data fidelity by mixing the spectrum of different speakers in a guided way, which can not only ensure that the central discriminative regions of the spectrum are always retained after the mixing operation, but also the pasting patches from the different spectrums are effective enough. This replacement strategy allows us to generate more reasonable training samples. At last, we refine the model trained with GuidedMix with un‐augmented data to balance robustness and accuracy. Experiments on VoxCeleb indicate that GuidedMix improves performance under clean, noisy, and short‐duration test scenarios and can be well combined with offline data augmentation.