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Application of Artificial Intelligence on Post Pandemic Situation and Lesson Learn for Future Prospects., , , , and . J. Exp. Theor. Artif. Intell., 35 (3): 327-344 (April 2023)Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification., , , , and . CoRR, (2019)Computationally Efficient Speaker Identification for Large Population Tasks using MLLR and Sufficient Statistics., , and . Odyssey, page 3. ISCA, (2010)Investigation of Speaker-Clustered UBMs based on Vocal Tract Lengths and MLLR matrices for Speaker Verification., and . Odyssey, page 13. ISCA, (2010)RedDots replayed: A new replay spoofing attack corpus for text-dependent speaker verification research., , , , , , , , , and 3 other author(s). ICASSP, page 5395-5399. IEEE, (2017)Use of VTL-wise models in feature-mapping framework to achieve performance of multiple-background models in speaker verification., and . ICASSP, page 4552-4555. IEEE, (2011)Multiple background models for speaker verification using the concept of vocal tract length and MLLR super-vector., and . I. J. Speech Technology, 15 (3): 351-364 (2012)Further optimisations of constant Q cepstral processing for integrated utterance and text-dependent speaker verification., , , , , , and . SLT, page 179-185. IEEE, (2016)Combination of Cepstral and Phonetically Discriminative Features for Speaker Verification., , , and . IEEE Signal Process. Lett., 21 (9): 1040-1044 (2014)rVAD: An unsupervised segment-based robust voice activity detection method., , and . Comput. Speech Lang., (2020)