  End-to-end (E2E) modeling is advantageous for automatic speech recognition
(ASR) especially for Japanese since word-based tokenization of Japanese is not
trivial, and E2E modeling is able to model character sequences directly. This
paper focuses on the latest E2E modeling techniques, and investigates their
performances on character-based Japanese ASR by conducting comparative
experiments. The results are analyzed and discussed in order to understand the
relative advantages of long short-term memory (LSTM), and Conformer models in
combination with connectionist temporal classification, transducer, and
attention-based loss functions. Furthermore, the paper investigates on
effectivity of the recent training techniques such as data augmentation
(SpecAugment), variational noise injection, and exponential moving average. The
best configuration found in the paper achieved the state-of-the-art character
error rates of 4.1%, 3.2%, and 3.5% for Corpus of Spontaneous Japanese (CSJ)
eval1, eval2, and eval3 tasks, respectively. The system is also shown to be
computationally efficient thanks to the efficiency of Conformer transducers.
