  As the character-based end-to-end automatic speech recognition (ASR) models
evolve, the choice of acoustic modeling units becomes important. Since Korean
is a fairly phonetic language and has a unique writing system with its own
Korean alphabet, it's worth investigating modeling units for an end-to-end
Korean ASR task. In this work, we introduce lexicon-free modeling units in
Korean, and explore them using a hybrid CTC/Attention-based encoder-decoder
model. Five lexicon-free units are investigated: Syllable-based Korean
character (with English character for a code-switching task), Korean Jamo
character (with English character), sub-word on syllable-based character (with
sub-word in English), sub-word on Jamo character (with sub-words in English),
and finally byte unit, which is a universal one across language. Experiments on
Zeroth-Korean (51.6 hrs) and Medical Record (2530 hrs) are done for Korean and
Korean-English code-switching ASR tasks, respectively. Sequence-to-sequence
learning with sub-words based on Korean syllables (and sub-words in English)
performs the best for both tasks without lexicon and an extra language model
integration.
