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Uncertainty Modeling Without Subspace Methods For Text-Dependent Speaker Recognition.

, , , , and . Odyssey, page 16-23. ISCA, (2016)

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Modelling speaker and channel variability using deep neural networks for robust speaker verification., , , and . SLT, page 192-198. IEEE, (2016)End-To-End Detection Of Attacks To Automatic Speaker Recognizers With Time-Attentive Light Convolutional Neural Networks., , and . MLSP, page 1-6. IEEE, (2019)Neural Embedding Extractors for Text-Independent Speaker Verification., , and . SPECOM, volume 13721 of Lecture Notes in Computer Science, page 10-23. Springer, (2022)Frequency-Coded Chipless RFID Tags: Notch Model, Detection, Angular Orientation, and Coverage Measurements., , , , and . Sensors, 20 (7): 1843 (2020)Development of Voice Spoofing Detection Systems for 2019 Edition of Automatic Speaker Verification and Countermeasures Challenge., and . ASRU, page 1003-1010. IEEE, (2019)CAMSAT: Augmentation Mix and Self-Augmented Training Clustering for Self-Supervised Speaker Recognition., and . ASRU, page 1-8. IEEE, (2023)On the Use of Fisher Vector Encoding for Voice Spoofing Detection.. UCAmI, volume 31 of MDPI Proceedings, page 37. MDPI, (2019)Deeply Fused Speaker Embeddings for Text-Independent Speaker Verification., , , and . INTERSPEECH, page 3588-3592. ISCA, (2018)Uncertainty Modeling Without Subspace Methods For Text-Dependent Speaker Recognition., , , , and . Odyssey, page 16-23. ISCA, (2016)Analysis of ABC Submission to NIST SRE 2019 CMN and VAST Challenge., , , , , , , , , and 12 other author(s). Odyssey, page 289-295. ISCA, (2020)