End-to-End Language Identification Using Attention-Based Recurrent Neural Networks

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Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System (2018, methodology): “In [26], attention-based recurrent neural network (RNN) is introduced to get utterance level representation for closed-set LR task .”
Knowledge Distillation-Based Representation Learning for Short-Utterance Spoken Language Identification (2020, background): “(RNNs), convolutional neural networks (CNNs), and attentionbased neural networks have been investigated on LID tasks [12]– [16].”
Feature Representation of Short Utterances Based on Knowledge Distillation for Spoken Language Identification (2018, background): “Recently, endto-end approaches with convolutional neural networks (CNN), recurrent neural networks (RNN), and attention-based neural networks have been investigated on LID tasks[11, 12, 13, 14].”
Interactive Learning of Teacher-student Model for Short Utterance Spoken Language Identification (2019, background): “Recently, end-to-end approaches with deep neural networks (DNN), recurrent neural networks (RNN), convolutional neural networks (CNN) and attention-based neural networks have been investigated on LID tasks [9, 10, 11, 12, 13].”