Efficient Lightweight Speaker Verification With Broadcasting CNN-Transformer and Knowledge Distillation Training of Self-Attention Maps

Developing a lightweight speaker embedding extractor (SEE) is crucial for the practical implementation of automatic speaker verification (ASV) systems. To this end, we recently introduced broadcasting convolutional neural networks (CNNs)-meet-vision-Transformers (BC-CMT), a lightweight SEE that utilizes broadcasted residual learning (BRL) within the hybrid CNN-Transformer architecture to maintain a small number of model parameters. We proposed three BC-CMT-based SEE with three different sizes: BC-CMT-Tiny, -Small, and -Base. In this study, we extend our previously proposed BC-CMT by introducing an improved model architecture and a training strategy based on knowledge distillation (KD) using self-attention (SA) maps. First, to reduce the computational costs and latency of the BC-CMT, the two-dimensional (2D) SA operations in the BC-CMT, which calculate the SA maps in the frequency–time dimensions, are simplified to 1D SA operations that consider only temporal importance. Moreover, to enhance the SA capability of the BC-CMT, the group convolution layers in the SA block are adjusted to have smaller number of groups and are combined with the BRL operations. Second, to improve the training effectiveness of the modified BC-CMT-Tiny, the SA maps of a pretrained large BC-CMT-Base are used for the KD to guide those of a smaller BC-CMT-Tiny. Because the attention map sizes of the modified BC-CMT models do not depend on the number of frequency bins or convolution channels, the proposed strategy enables KD between feature maps with different sizes. The experimental results demonstrate that the proposed BC-CMT-Tiny model having 271.44K model parameters achieved 36.8% and 9.3% reduction in floating point operations on 1s signals and equal error rate (EER) on VoxCeleb 1 testset, respectively, compared to the conventional BC-CMT-Tiny. The CPU and GPU running time of the proposed BC-CMT-Tiny ranges of 1 to 10 s signals were 29.07 to 146.32 ms and 36.01 to 206.43 ms, respectively. The proposed KD further reduced the EER by 15.5% with improved attention capability.