Abstract 1. Introduction 2. Model 2.1 End-to-End Network: CNN-RNN-Transducer 2.2 Encoder Design 3. E...
Abstract 1. Introduction 2. ConformerEncoder 2.1. Multi-Headed Self-Attention Module 2.2. Convolution Modul...
Abstact 1. Introduction 2. ConvNet Configurations 2.1 Architecture 2.2 Confiurations 2.3 Discussion ...
π Abstract β . Introduction β ‘. Methods 2.1 Few-shot learning framwork 2.2 Instance-wise aggregation 2.3...
π Abstract β . Introduction β ‘. Metric Learning Framework 2.1 Loss functions 2.2 Pair selection strategy ...
π Abstract β . Introduction β ‘. Baseline Architecture TE2E model β ’. Attention-based Model 3.1 Basi...
π Abstract β . Introduction π± β ‘. Related Work πΏ β ’. Proposed Approach π³ β £. Experiments and Results πΊ β €. Conclusion πKai ...
π Abstract β . Introduction 1.1 Background 1.2 TE2E β ‘. GE2E Model 2.1 Training Method 2...
π Abstract β . Introduction β ‘. Domain Adaption with GANs β ’. Generative Adversarial Speaker Embedding Networks 3....
π Abstract β . Introduction β ‘. Database and Baseline Systems β ’. Proposed End-to-End DNN Architecture β £. Results and Dis...
π Abstract π Introduction π Deep speaker embedding π High-order pooling with attention π Experimental settingsKoji Okab...
