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A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification.

, , , , and . ICASSP, page 5189-5193. IEEE, (2018)

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Generalized I-vector Representation with Phonetic Tokenizations and Tandem Features for both Text Independent and Text Dependent Speaker Verification., , , and . J. Signal Process. Syst., 82 (2): 207-215 (2016)A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data., and . APSIPA ASC, page 1133-1138. IEEE, (2021)Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System., , and . Odyssey, page 74-81. ISCA, (2018)End-to-end Language Identification using NetFV and NetVLAD., , , , , and . ISCSLP, page 319-323. IEEE, (2018)A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification., , , , and . ICASSP, page 5189-5193. IEEE, (2018)Locality sensitive discriminant analysis for speaker verification., , , and . APSIPA, page 1-5. IEEE, (2016)Insights into End-to-End Learning Scheme for Language Identification., , , , and . CoRR, (2018)Survey Talk: End-to-End Deep Neural Network Based Speaker and Language Recognition., , and . INTERSPEECH, ISCA, (2019)Text-independent voice conversion using deep neural network based phonetic level features., , , , and . ICPR, page 2872-2877. IEEE, (2016)A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data., and . CoRR, (2020)