# Code-Switching in Automatic Speech Recognition: The Issues and Future Directions
## 5 Discussion
A critical process in any speech recognition system development is the acoustic modelling of speech. Over the years, many techniques have been employed to develop the acoustic model for monolingual, bilingual, and multilingual speech recognition systems. The development of the acoustic model for the bilingual and multilingual speech recognition systems that contain CS requires the model to switch languages during the recognition of speech, which can happen at any point within a sentence. The ability of the ASR system to identify the CS occurrence determines the recognition performance of the system. This can be challenging due to the variation in ways a speaker can CS during a conversation. As such, the ASR system requires sufficient coverage of CS speech in real life. Using naturally recorded speech such as broadcasts and podcasts is one possible solution. In addition, having the ASR system predict the occurrence of CS in speech from the linguistic point of view will help the ASR system to improve its recognition performance. The CS for languages that differ in terms of linguistic, pronunciation and speech features can be better predicted for CS occurrence than for languages that have high similarity in terms of linguistic, pronunciation and speech features. The use of semantic analysis may help ASR systems to improve their CS recognition performance for languages that share similar linguistic, pronunciation and speech features. Most papers use supervised and semi-supervised training to develop acoustic and language models. Deep learning algorithms such as the neural network and recurrent neural network language modelling are common for CS. Existing techniques such as the neural network reduce the sample size and the acoustic model's complexity for the monolingual ASR system. However, this can be a problem in CS recognition due to the unification of similar vocabulary for both languages, resulting in lower recognition accuracy. The development of CS ASR systems needs to balance language coverage and model complexity. The CS phone sets in bilingual and multilingual speech recognition can be made in three distinctive ways: combining, mapping, and merging similar phone sets of the languages. Researchers prefer combining the acoustic models of each monolingual model for both the bilingual and multilingual ASR systems. Combining the monolingual models for CS is typical for well-resourced languages. Merging of acoustic models was applied in CS involving low-resourced languages [4; 5; 52]. The merging of similar phones of the two languages for CS reduces the size and complexity of the CS. Mapping was applied in some research where the languages share common graphemes, such as Indonesian and Arabic. In the earlier section, we presented the overall results of the evaluation of ASR performance in recognising speech with CS, conducted in the selected papers. Unlike monolingual ASR, where the standard evaluation metric is the WER, the CS evaluation uses several different measurements, such as the PER, CER, and MER, depending on the type of languages being tested. For example, the WER is typical for languages that use the Roman alphabet in many European countries, while the CER is ubiquitous in CS for English and Mandarin, with different written formats. In ASR research, it is difficult to make an \"apples to apples\" comparison between the methods due to variations in the inputs such as database size and the number of speakers, among others. The performance of ASR in recognising CS may not be entirely due to the techniques used for training the models, but also to the size and the quality of the database. It is important for researchers to differentiate the improvement to the WER that is caused by the proposed technique as well as the quality of the database used. We also found that depending entirely on the WER as the standard measure of performance of ASR systems may not give readers the complete picture of the merits and demerits of a particular technique applied in developing an ASR system for handling CS.