Results 31 to 40 of about 11,913 (306)
IEEE Access, 2021 In this paper, we propose self-supervised speaker representation learning strategies, which comprise of a bootstrap equilibrium speaker representation learning in the front-end and an uncertainty-aware probabilistic speaker embedding training in the back-
Sung Hwan Mun +4 more
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Learnable MFCCs for Speaker Verification [PDF]
2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021 Accepted to ISCAS ...
Xuechen Liu 0001 +2 more
openaire +4 more sources
Dynamic visual features for audio-visual speaker verification [PDF]
, 2010 The cascading appearance-based (CAB) feature extraction technique has established itself as the state-of-the-art in extracting dynamic visual speech features for speech recognition.
Dean, David B. +3 more
core +1 more source
Employing Emotion Cues to Verify Speakers in Emotional Talking Environments
Journal of Intelligent Systems, 2016 Usually, people talk neutrally in environments where there are no abnormal talking conditions such as stress and emotion. Other emotional conditions that might affect people’s talking tone include happiness, anger, and sadness. Such emotions are directly
Shahin Ismail
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The Achilles’ heel of spoofing-aware speaker verification: a module-level analysis of adversarial vulnerabilities [PDF]
PeerJ Computer ScienceSpoofing-aware speaker verification (SASV) systems combine automatic speaker verification (ASV) and spoofing countermeasure (CM) modules to defend against spoofing attacks. However, their vulnerability to adversarial attacks remains underexplored.
Yowon Lee +3 more
doaj +2 more sources
IEEE Access, 2017 Environmental noise and reverberation conditions severely degrade the performance of forensic speaker verification. Robust feature extraction plays an important role in improving forensic speaker verification performance.
Ahmed Kamil Hasan Al-Ali +4 more
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Addressing Text-Dependent Speaker Verification Using Singing Speech
Applied Sciences, 2019 The automatic speaker verification (ASV) has achieved significant progress in recent years. However, it is still very challenging to generalize the ASV technologies to new, unknown and spoofing conditions.
Yan Shi +4 more
doaj +1 more source
astorfi/3D-convolutional-speaker-recognition: 3D Convolutional Neural Networks for Speaker Verification [PDF]
, 2017 <p>This project is aimed to provide the implementation for Speaker Verification (SR) by using 3D convolutional neural networks following the SR protocol.</p ...
Amirsina Torfi
core +1 more source
Dilated residual networks with multi-level attention for speaker verification [PDF]
, 2020 With the development of deep learning techniques, speaker verification (SV) systems based on deep neural network (DNN) achieve competitive performance compared with traditional i-vector-based works. Previous DNN-based SV methods usually employ time-delay
Xu, J, Guo, C, Wu, Y, Gao, H, Bai, G
core +1 more source
Speaker Recognition from Emotional Speech Using I-vector Approach [PDF]
Journal of Electrical and Electronics Engineering, 2014 In recent years the concept of i-vectors become very popular and successful in the field of the speaker verification. The basic principle of i-vectors is that each utterance is represented by fixed-length feature vector of low-dimension.
MACKOVÁ Lenka, CIŽMÁR Anton
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