# Code-Switching in Automatic Speech Recognition: The Issues and Future Directions
## 4 Results of Review
Table 1 shows the direction of the selected papers in terms of focus, databases, acoustics and language modelling, and evaluation metrics. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & **Focus** & \multicolumn{3}{c}{**Database**} & \multicolumn{3}{c}{**Acoustics \&**} & \multicolumn{3}{c}{**Evaluation Metrics**} & \\ \cline{2-13}  & & & & & & & & & & & & & \\ \hline
[2] & ✓ & & ✓ & & ✓ & ✓ & & & & & & ✓ & \\ \hline
[3] & ✓ & & ✓ & & & ✓ & ✓ & & & ✓ & & & \\ \hline  
is not challenging in bilingual and multilingual ASR systems for well-resourced languages [28; 44; 45]. Bilingual and multilingual speech recognition faces challenges in the availability of databases for under-resourced languages, with many having to first develop their databases [5; 34; 36; 64; 65; 39]. However, the continuing interest in CS has resulted in the development of standard databases in recent research on CS [2; 4; 6; 30; 40; 47; 51; 52; 53; 54; 55]. The existing monolingual standard databases have speech utterances in a single language. On the other hand, the CS database is a compilation of speech that contains words from two languages in the same utterance. [PERSON] et al. [39] recently compiled a CS speech corpus containing 14.3 h of language-balanced speech from soap opera broadcasts, and a Dutch broadcast database, which contained 17.5 and 89.5 h of Dutch data, respectively, while the English Broadcast News Database (HUB4) is the primary source of English broadcast data [4]. Separately, [PERSON] et al. [47] developed a bilingual lexicon containing more than 100,000 Frisian and Dutch words and about 160,000 lexicon entries due to the words with multiple phonetic transcriptions. Of the 22 papers that investigated CS, 10 of the papers (45%) developed their CS databases, while the balance (55%) made use of the existing speech databases such as FAME (Frisian Audio Mining Enterprise) [4; 5; 47] and SEAME (South East Asia Mandarin-English) corpus [32; 40; 55; 58]. Table 2 mentions details of the CS databases used in the selected research. Most CS databases cover two languages, though some databases have CS for more than two languages. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Article No. **} & \multicolumn{5}{c}{**Database**} \\ \cline{2-6}  & **Existing Database** & **Self-Developed** & **Size (HOURS)** & **Number of Speakers** & **Number of Languages** \\ \hline
[2] & ✓ & & NP & NP & 2 \\ \hline
[4] & ✓ & & 11.8 & NP & 3 \\ \hline
[5] & ✓ & & 11.8 & NP & 2 \\ \hline
[6] & & ✓ & NP & NP & 2 \\ \hline
[26] & & ✓ & NP & NP & 3 \\ \hline
[29] & ✓ & & 200  
Most of the databases for CS have recorded utterances of more than 10 h and involve many speakers uttering two languages. Some researchers developed the CS database artificially using the text-to-speech (TTS) system [6; 26; 30; 50]. It can be concluded that the speech database development for CS is on par with the monolingual ASR system. Many researchers also use the existing TTS system to generate CS speech. CS involves the undersourced languages lacking adequate databases [5; 39; 41]. Many of the under-resourced languages have no existing TTS system that can support the development of CS speech utterances. However, the review shows that under-resourced languages such as Sepedi have CS databases for multilingual speech recognition research [41].