MCSAE: Masked Cross Self-Attentive Encoding for Speaker Embedding
This work addresses speaker recognition by enhancing embedding quality, representing an incremental improvement over previous self-attentive and state-of-the-art methods.
The paper tackled the problem of speaker embedding encoding by proposing Masked Cross Self-Attentive Encoding (MCSAE) to incorporate both high-level and low-level features, resulting in improved performance with an equal error rate of 2.63% and a minimum detection cost function of 0.1453 on the VoxCeleb1 dataset.
In general, a self-attention mechanism has been applied for speaker embedding encoding. Previous studies focused on training the self-attention in a high-level layer, such as the last pooling layer. However, the effect of low-level features was reduced in the speaker embedding encoding. Therefore, we propose masked cross self-attentive encoding (MCSAE) using ResNet. It focuses on the features of both high-level and lowlevel layers. Based on multi-layer aggregation, the output features of each residual layer are used for the MCSAE. In the MCSAE, cross self-attention module is trained the interdependence of each input features. A random masking regularization module also applied to preventing overfitting problem. As such, the MCSAE enhances the weight of frames representing the speaker information. Then, the output features are concatenated and encoded to the speaker embedding. Therefore, a more informative speaker embedding is encoded by using the MCSAE. The experimental results showed an equal error rate of 2.63% and a minimum detection cost function of 0.1453 using the VoxCeleb1 evaluation dataset. These were improved performances compared with the previous self-attentive encoding and state-of-the-art encoding methods.