
The author utilizes the TitaNet model trained with additive angular margin (AAM) loss to optimize cosine distance between speaker embeddings, using cosine similarity as the back-end metric.


coremsg

In this paper, we propose TitaNet, a novel neural network architecture for extracting speaker representations.
We employ 1D depth-wise separable convolutions with Squeeze-and-Excitation (SE) layers with global context …

TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context

TitaNet-Neural-Model-for-Speaker-Representation-with-AAM-Loss-and-Cosine-Similarity


TitaNet: Neural Model for Speaker Representation with AAM Loss and Cosine Similarity
