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Speaker recognition based on deep learning: An overview
Speaker recognition is a task of identifying persons from their voices. Recently, deep
learning has dramatically revolutionized speaker recognition. However, there is lack of …
learning has dramatically revolutionized speaker recognition. However, there is lack of …
Deep speaker embeddings for speaker verification: Review and experimental comparison
The construction of speaker-specific acoustic models for automatic speaker recognition is
almost exclusively based on deep neural network-based speaker embeddings. This work …
almost exclusively based on deep neural network-based speaker embeddings. This work …
MFA: TDNN with multi-scale frequency-channel attention for text-independent speaker verification with short utterances
The time delay neural network (TDNN) represents one of the state-of-the-art of neural
solutions to text-independent speaker verification. However, they require a large number of …
solutions to text-independent speaker verification. However, they require a large number of …
Multi-query multi-head attention pooling and inter-topk penalty for speaker verification
This paper describes the multi-query multi-head attention (MQMHA) pooling and inter-topK
penalty methods which were first proposed in our submitted system description for VoxCeleb …
penalty methods which were first proposed in our submitted system description for VoxCeleb …
Scoring of large-margin embeddings for speaker verification: Cosine or PLDA?
The emergence of large-margin softmax cross-entropy losses in training deep speaker
embedding neural networks has triggered a gradual shift from parametric back-ends to a …
embedding neural networks has triggered a gradual shift from parametric back-ends to a …
Duality temporal-channel-frequency attention enhanced speaker representation learning
The use of channel-wise attention in CNN based speaker representation networks has
achieved remarkable performance in speaker verification (SV). But these approaches do …
achieved remarkable performance in speaker verification (SV). But these approaches do …
Cosine Scoring with Uncertainty for Neural Speaker Embedding
Uncertainty modeling in speaker representation aims to learn the variability present in
speech utterances. While the conventional cosine-scoring is computationally efficient and …
speech utterances. While the conventional cosine-scoring is computationally efficient and …
[PDF][PDF] Joint Feature Enhancement and Speaker Recognition with Multi-Objective Task-Oriented Network.
Recently, increasing attention has been paid to the joint training of upstream and
downstream tasks, and to address the challenge of how to synchronize various loss …
downstream tasks, and to address the challenge of how to synchronize various loss …
RSKNet-MTSP: Effective and portable deep architecture for speaker verification
Y Wu, C Guo, J Zhao, X **, J Xu - Neurocomputing, 2022 - Elsevier
The convolutional neural network (CNN) based approaches have shown great success for
speaker verification (SV) tasks, where modeling long temporal context and reducing …
speaker verification (SV) tasks, where modeling long temporal context and reducing …
Adaptive margin circle loss for speaker verification
Deep-Neural-Network (DNN) based speaker verification sys-tems use the angular softmax
loss with margin penalties toenhance the intra-class compactness of speaker embeddings …
loss with margin penalties toenhance the intra-class compactness of speaker embeddings …