# Code-Switching in Automatic Speech Recognition: The Issues and Future Directions
## 4 Results of Review
#### 4.2.2 Acoustic and Language Modelling
The proposed method has the advantage that the existing acoustic model with only Indonesian phones is used to build the speech recognition system. [PERSON] et al. [57] used the universal phone set (UPS), a machine-readable phone set based on the IPA, to represent the language's universal speech units. Generally, there is a one-to-one mapping between UPS and IPA symbols, while UPS is a superset of IPA in a few other cases. Similarly, ref. [31] proposed a method that does not require the training of language-specific acoustic models. The method created a new phoneme set to obtain multilingual acoustic modelling by sharing part of language-specific phonemes with other languages. However, sharing of phonemes increases the amount of training data. The acoustic model with the shared phoneme set can perform speech recognition for a minor (low-resource language) utterance. * Other techniques  
Recently, end-to-end (E2E) systems have been preferred by researchers for their simplicity and success in multilingual [5; 29; 41; 46; 51; 52; 53; 60] settings. An E2E system directly maps an input sequence of acoustic features to an output sequence of characters, phonemes, or words. Two common variants of the E2E framework are (i) the connectionist temporal classification (CTC) [31; 47], and (ii) the sequence-to-sequence modelling with an attention mechanism [41]. In E2E, the model is trained with characters as the output targets and does not include any explicit pronunciation model or language model, which means that the E2E does not need phonetically labelled training data during development. On top of that, the E2E system predicts phones or characters directly from acoustic information without any form of manually prepared alignment, making it a suitable method for multiple languages speech recognition and CS speech recognition [41]. Some common architectures for E2E are connectionist temporal classification (CTC), attention-based encoder-decoder networks, and recurrent neural network transducers [5; 60; 51; 66]. The performance of E2E systems depends on the training data's size (it needs massive data), which can be a problem for CS for poorly resourced languages. [PERSON] et al. [52] introduced a hybrid attention/CTC model that used language identification explicitly and joint language identification to predict the CS between languages. Similarly, ref. [53] adopted the attention-based E2E model for the Mandarin-English CS, with three improvements, which are: (1) multi-task learning for language identity information, (2) word pieces as English modelling units to reduce the modelling unit gap between Mandarin and English, and (3) transfer learning to further improve the performance by using the larger size of Mandarin and English monolingual data. In ref. [55], the E2E and CTC were used for CS Korean-English languages. [PERSON] et al. [5] proposed the unsupervised two-step approach to language modelling. Unlike the traditional hybrid HMM-DNN system, an E2E CTC acoustic model is not trained using frame-level labels concerning the Cross-Entropy (CE) criterion. The model automatically learns the alignments between speech frames and label sequences using the CTC objective. This approach predicts the conditional probability of the label sequence by adding the joint probabilities of the corresponding set of CTC symbol sequences [5]. [PERSON] et al. [30] propose the transliteration optimised word error rate to normalise the data for three types of language models, (1) a conventional n-gram language model, (2) a maximum entropy-based language model, and (3) a long short-term memory (LSTM) language model, in the CTC environment [30].