# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 4 Advances in Speech Recognition for Uyghur, Kazakh, and Kyrgyz
### Technical Development  
#### 4.2.1 Uyghur  
Speech recognition in Uyghur started quite early. [PERSON] et al. constructed a Uyghur database in 1996 [119] and, then, performed an in-depth study of Uyghur speech recognition techniques. For instance, in 2003, they studied the performance of different recognition units under the HMM framework and found that syllables were suitable units for acoustic modeling [120]. [PERSON] et al. [121] discussed the construction principle of the Uyghur database in 2009 and also studied the speech recognition approach based on the HMM and the speech synthesis approach based on unit selection. In 2010, [PERSON] et al. [122] compared two Uyghur speech recognition systems based on morphemes and words, respectively. Experiments conducted on 150 h of microphone data from 353 speakers showed that the performance of the word-based system was superior. In the same year, [PERSON] et al. [123] built a large-scale Uyghur speech recognition system based on stem-suffix. Experiments conducted on 500 h of telephone data from 835 speakers found that the system based on stem-suffix obtained better performance than the system based on words. After 2015, deep neural networks began to be applied to Uyghur speech recognition. For example, [PERSON] et al. [124] trained a DNN-HMM model with 50 h of data collected by USTC and reported better performance than the GMM-HMM (21.82%->12.98%). [PERSON] et al. [125] trained a DNN-HMM model with 4466 utterances collected privately and reported more significant performance gains over the GMM-HMM (40.18%->9.09%). In 2020, [PERSON] et al. [126] built a novel end-to-end speech recognition system based on the Transformer+CTC architecture with the King-ASR450 database. In 2021, [PERSON] et al. [127] implemented a Conformer system. Since then, the ASR research on Uyghur has kept up with the technical advance of common languages. In addition to following state-of-the-art technology, researchers have also made some special designs according to the individualities of Uyghur. Firstly, to solve the data scarcity issue, researchers put forward several schemes. In 2017, [PERSON] et al. [128] verified the value of transfer learning in Uyghur speech recognition. They trained a basic BLSTM-HMM model with Chinese data and, then, retrained the output layer with Uyghur data. Since the volume of Uyghur data was already large, the contribution of the transfer learning was not clear. However, significant improvement was soon observed in data sparsity scenarios [129; 130]. Another representative work towards data efficiency was conducted by [PERSON] et al. [131] based on semi-supervised training. They firstly employed the transfer learning method to convert a Chinese model into a Uyghur model, by fine-tuning with a small amount of annotated Uyghur data. The obtained model was then used to decode a large amount of unlabeled Uyghur data, and the decoding results were used to refine the Uyghur model. They found that, with this method, using only 500 annotated utterances, they could achieve more competitive performance than the model trained from scratch with 50 h of annotated data. Secondly, since the vocabulary of Uyghur is very large, researchers have proposed to use subword units in language modeling (LM). [PERSON] et al. [132] developed a multi