title: 4D ASR: Joint modeling of CTC, Attention, Transducer, and Mask-Predict decoders
abstract: The network architecture of end-to-end (E2E) automatic speech recognition (ASR) can be classified into several models, including connectionist temporal classification (CTC), recurrent neural network transducer (RNN-T), attention mechanism, and non-autoregressive mask-predict models. Since each of these network architectures has pros and cons, a typical use case is to switch these separate models depending on the application requirement, resulting in the increased overhead of maintaining all mode
journal: arXiv (Cornell University)