A Comparative Study on Neural Architectures and Training Methods for\n Japanese Speech Recognition
Journal: arXiv (Cornell University)
Year: 2021

End-to-end (E2E) modeling is advantageous for automatic speech recognition\n(ASR) especially for Japanese since word-based tokenization of Japanese is not\ntrivial, and E2E modeling is able to model character sequences directly. This\npaper focuses on the latest E2E modeling techniques, and investigates their\nperformances on character-based Japanese ASR by conducting comparative\nexperiments. The results are analyzed and discussed in order to understand the\nrelative advantages of long short-te