# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 2 Low-Resource Language Speech Recognition
### Methods of Low-Resource Language Speech Recognition
#### 2.2.3 Feature Sharing  
With the emergence of deep learning, feature sharing becomes a standard and primary approach for low-resource language speech recognition. The basic idea of this method is that human languages are similar in speech production so that the acoustic features of different languages can be largely shared. Three approaches are commonly used to share the features in practice [57]: (1) to learn a language-independent feature extraction network and, then, use the network to extract features for low-resource languages. This approach is mostly applied in the tandem ASR framework [58], and it firstly trains a DNN model with rich-resource language monolingual or multilingual data, then it uses this DNN model to extract language-independent acoustic features, and finally, it trains the GMM-HMM or DNN-HMM for low-resource languages [59; 60; 61; 62; 63]. (2) Multilingual learning: This approach is mostly based on the hybrid ASR framework [31]. The main architecture is a multi-head DNN trained with the speech data of multiple languages including the target language, where the feature extractor is shared and each language maintains its own phoneme recognition layer [57; 64]. (3) Transfer learning: This firstly uses rich-resource language monolingual or multilingual data to train a neural network, and then, it uses this to initialize the model for the low-resource language. A fine-tuning step then follows to adapt the model to the data of the low-resource language [65]. In recent years, end-to-end speech recognition technology has grown popular, and how to transfer end-to-end models to low-resource language speech recognition has become a research focus. [PERSON] et al. [66] trained a CTC model with the multilingual data in the BABEL dataset, showing that the multilingual training improved the performance \begin{table}
\begin{tabular}{l l} \hline \hline
**Method** & **Summary** \\ \hline Unsupervised learning & Using semi-supervised or self-supervised learning approach to utilize unlabelled data. \\ \hline Completely unsupervised learning & No transcribed data at all, employ unparallel speech and text to train ASR systems. \\ \hline Massively multilingual modeling & Utilize speech data and lexical knowledge of a large amount of languages, to train models for ‘any’ language (nearly). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._for each language. [PERSON] et al. [67] built a multilingual attention model. They trained this attention model using the data from 10 languages in the BABEL dataset and, then, migrated it to the other five languages in BABEL. Similar to the scheme proposed by [67], [PERSON] et al. [68] constructed a multilingual CTC-attention model. [PERSON] et al. [69] adopted this multilingual CTC-attention model and reported better results than monolingual models when experimented on the BABEL dataset. [PERSON] et al. [70] verified the performance of multilingual Transformer models. It was based on multilingual character sets and used the BPE algorithm to generate subword units ([[https://github.com/rsennrich/subword-nmt](https://github.com/rsennrich/subword-nmt)]([https://github.com/rsennrich/subword-nmt](https://github.com/rsennrich/subword-nmt)), accessed on 25 December 2022). Experimental results on six languages in the CALLHOME dataset showed that the Transformer model trained on multilingual data could achieve better results over monolingual models. [PERSON] et al. [71] also verified the multilingual Transformer model, and in particular, they found that involving language information can further improve the model's strength. [PERSON] et al. [72] also demonstrated the importance of language information. They added a learnable language vector to the DNN and found that the performance on low-resource languages was improved. The basic assumption of feature sharing is that the different languages are similar in speech generation. This idea is not new and was also studied in the context of the traditional GMM-HMM framework. For example, [PERSON] et al.