Speaker verification and spoken language identification using a generalized i-vector framework with phonetic tokenizations and tandem features

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Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System (2018, methodology): “…perceptual linear prediction (PLP), shifted delta coefﬁ-cients (SDC) features [2, 5], and automatically learned phoneme discriminant features from deep neural networks (DNN), such as bottleneck features [6, 7, 8], phoneme posterior probability (PPP) features [9], and tandem features [10, 11].”
Deep bottleneck network based i-vector representation for language identification (2015, background): “The generalization capability of the framework is also validated separately for DBNs trained on either an English or a Mandarin corpus.”
A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification (2018, background): “Another way to learn the dictionary is through phonetically-aware supervised training [6, 7].”