# Exploring Lexicon-Free Modeling Units for End-to-End Korean and Korean-English Code-Switching Speech Recognition

arXiv: https://arxiv.org/abs/1910.11590 · HTML: https://mlsanity.com/paper/1910.11590/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [Massively Multilingual ASR on 70 Languages: Tokenization, Architecture, and Generalization Capabilities](https://mlsanity.com/paper/2211.05756/) (2022)
- [K-Wav2vec 2.0: Automatic Speech Recognition based on Joint Decoding of Graphemes and Syllables](https://mlsanity.com/paper/2110.05172/) (2021)
- [Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices](https://mlsanity.com/paper/2406.02562/) (2024)
- [Romanization Encoding For Multilingual ASR](https://mlsanity.com/paper/2407.04368/) (2024)

## Abstract (arXiv)

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.