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Found 5 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
End-to-end User Recognition using Touchscreen Biometrics
In the proposed system data from the touchscreen goes directly, without any processing, to the input of a deep neural network, which is able to decide on the identity of the user.
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.
Deep Learning for Single and Multi-Session i-Vector Speaker Recognition
Given i-vectors as inputs, the authors proposed an impostor selection algorithm and a universal model adaptation process in a hybrid system based on Deep Belief Networks (DBN) and Deep Neural Networks (DNN) to discriminatively model each target speaker.
