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The network architecture of end-to-end (E2E) automatic speech recognition(ASR) can be classified into several models, including connectionist temporalclassification (CTC), recurrent neural network transducer (RNN-T), attentionmechanism, and non-autoregressive mask-predict models. Since each of thesenetwork architectures has pros and cons, a typical use case is to switch theseseparate models depending on the application requirement, resulting in theincreased overhead of maintaining all models. Several methods for integratingtwo of these complementary models to mitigate the overhead issue have beenproposed; however, if we integrate more models, we will further benefit fromthese complementary models and realize broader applications with a singlesystem. This paper proposes four-decoder joint modeling (4D) of CTC, attention,RNN-T, and mask-predict, which has the following three advantages: 1) The fourdecoders are jointly trained so that they can be easily switched depending onthe application scenarios. 2) Joint training may bring model regularization andimprove the model robustness thanks to their complementary properties. 3) Novelone-pass joint decoding methods using CTC, attention, and RNN-T furtherimproves the performance. The experimental results showed that the proposedmodel consistently reduced the WER.

4D ASR: Joint modeling of CTC, Attention, Transducer, and Mask-Predict decoders