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Found 3 papers, 1 papers with code
Self-attention encoding and pooling for speaker recognition
On the other hand, self-attention networks based on Transformer architecture have attracted remarkable interests due to their high parallelization capabilities and strong performance on a variety of Natural Language Processing (NLP) applications.
Double Multi-Head Attention for Speaker Verification
In this paper we present Double Multi-Head Attention pooling, which extends our previous approach based on Self Multi-Head Attention.
Audio and Speech Processing Sound
Self Multi-Head Attention for Speaker Recognition
Most state-of-the-art Deep Learning (DL) approaches for speaker recognition work on a short utterance level.
