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Recently, several types of end-to-end speech recognition methods namedtransformer-transducer were introduced. According to those kinds of methods,transcription networks are generally modeled by transformer-based neuralnetworks, while prediction networks could be modeled by either transformers orrecurrent neural networks (RNN). This paper explores multitask learning, jointoptimization, and joint decoding methods for transformer-RNN-transducersystems. Our proposed methods have the main advantage in that the model canmaintain information on the large text corpus. We prove their effectiveness byperforming experiments utilizing the well-known ESPNET toolkit for the widelyused Librispeech datasets. We also show that the proposed methods can reduceword error rate (WER) by 16.6 % and 13.3 % for test-clean and test-otherdatasets, respectively, without changing the overall model structure norexploiting an external LM.

Multitask Learning and Joint Optimization for Transformer-RNN-Transducer Speech Recognition