## 6 Conclusions  
In the case of Korean, English words pronounced by Korean speakers--Korean-style English (i.e., Konglish)--have many phonetic variations from native-like English pronunciation. Moreover, mixed-language spoken data are very rare, making any model biased toward Korean, even if there are many data. Pronunciation variations and imbalanced data are major problems that degrade the recognition of CS speech. In this paper, we proposed pronunciation variations reflecting English words spoken by Koreans and the LM adaptation based on similarity of meaning. First, we tried to find a unified pronunciation model based on phonetic knowledge and deep learning by applying the language identification (LID) of [PERSON] et al. (2018). Despite this, there were problems with intrusions occurring between languages. However, our proposed method can avoid this problem. Secondly, we extracted the CS sentences that were semantically similar to the target domain and then applied the language model (LM) adaptation to solve the biased modeling toward Korean due to the imbalanced training data. [PERSON] et al. (2018) utilized a speech chain framework based on deep learning to enable ASR and TTS to learn code-switching. Although this closed-loop architecture improves the performance even without any parallel code-switching data, there is a limit to improving the performance when only using synthetic speech due to the quality. It seems that the performance can be improved if this method is combined with our method. Compared with the KR baseline, the proposed hybrid method (e.g., knowledge and deep learning) showed up to 11.6% improvement in the error reduction rate (ERR). Through the semantically similar sentence extraction process, we were able to obtain 16.5%, 15.0%, and 16.9% improvements in ERR in the experiments of LM adaptation. If all domain DBs were combined, the ERR improved by up to 17.3%. LM adaptation using the proposed method might be one way to solve the biased data problem, which is critical. However, although we dealt with some critical issues, if compared to the closed test, which would be the upper bound of the performance, there is still room for improvement. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Set Names**} & \multicolumn{3}{c}{**CA (\%)**} & \multicolumn{3}{c}{**ERR 1 (\%)**} \\ \cline{2-7}  & **Eco 2** & **AI 2** & **AI 2** & **Eco 2** & **AI 1 2** & **AI 2 2** \\ \hline KR Baseline & 71.0 & 76.4 & 77.4 & - & - & - \\ Konglish Native-like EN & 74.3 & 77.7 & 78.5 & 11.6 & 5.8 & 4.5 \\ +Base LM (K-Base LM) & 75.1 & 78.0 & 77.8 & 14.3 & 6.6 & 1.5 \\ K-Base LM + DA 1 & 75.8 & 79.2 & 79.3 & 16.5 & 12.0 & 8.4 \\ K-Base LM + DA 2 & 75.3 & 79.4 & 80.0 & 15.0 & 12.7 & 11.1 \\ K-Base LM + DA 3 & 75.9 & 79.8 & 79.8 & 16.9 & 14.2 & 10.5 \\ K-Base LM + DA 1 + 2 + 3 & 76.0 & 80.0 & 80.3 & 17.3 & 14.8 & 12.5 \\ (Closed Test) 3 & (83.1) & (85.7) & (85.9) & - & - & - \\ \hline \hline \end{tabular}  
* Error reduction rate (ERR) calculated by both KR baseline and another set (e.g., K-Base LM + DA 2 or K-Base LM + DA 3). 2 Eco is an acronym of Economy; AI1 is known as AILeC1; AI2 is the short form of AILeC2. 3 Used the shallow fusion method as the closed test; it was excluded from the other sets.  
\end{table} 
Table 6: Character accuracy (CA) of the domain adaptation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Correction Rate (\%)** & **Economy** & **AILec1** & **AILec2** \\ \hline KR Baseline & 32.7 & 42.0 & 38.3 \\ Konglish Native-like EN & 42.9 & 44.4 & 44.0 \\ +Base LM (K-Base LM) & 46.7 & 50.4 & 46.8 \\ K-Base LM + DA 1 & 51.2 & 58.6 & 61.4 \\ K-Base LM + DA 2 & 51.9 & 63.3 & 63.8 \\ K-Base LM + DA 3 & 52.1 & 59.5 & 61.1 \\ K-Base LM + DA 1 + 2 + 3 1 & 54.0 & 60.7 & 62.5 \\ \hline \hline \end{tabular}  
* Sum of domain adaptations 1, 2, and 3.  
\end{table} 
Table 7: ASR correction rate of English words with Hangul in Korean and English CS text. Recently, Tacotron [35], which is a kind of text-to-speech (TTS) system, produced very high-quality synthesized speech.