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Analyzing Speaker Verification Embedding Extractors and Back-Ends Under Language and Channel Mismatch.

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Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition., , , , and . CoRR, (2018)On the use of DNN Autoencoder for Robust Speaker Recognition., , , and . CoRR, (2018)Analysis of DNN approaches to speaker identification., , , , , , and . ICASSP, page 5100-5104. IEEE, (2016)Neural network based language models for highly inflective languages., , , , and . ICASSP, page 4725-4728. IEEE, (2009)Analysis of Speaker Recognition Systems in Realistic Scenarios of the SITW 2016 Challenge., , , , , and . INTERSPEECH, page 828-832. ISCA, (2016)Analysis of the but Diarization System for Voxconverse Challenge., , , , , , and . ICASSP, page 5819-5823. IEEE, (2021)Speaker vectors from subspace Gaussian mixture model as complementary features for language identification., , , , , , and . Odyssey, page 330-333. ISCA, (2012)Deep Neural Networks and Hidden Markov Models in i-vector-based Text-Dependent Speaker Verification., , , , and . Odyssey, page 24-30. ISCA, (2016)Analyzing Speaker Verification Embedding Extractors and Back-Ends Under Language and Channel Mismatch., , , , , , , , and . Odyssey, page 9-16. ISCA, (2022)Development of ABC Systems for the 2021 Edition of NIST Speaker Recognition Evaluation., , , , , , , , , and 5 other author(s). Odyssey, page 346-353. ISCA, (2022)