# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 5 Conclusions and Discussions  
Uyghur, Kazakh, and Kyrgyz all belong to the same language family and branch. Each language has its development path, but they also interact with each other. Especially in China, these three languages share many commonalities in terms of writing, pronunciation, and syntax [2]. From the perspective of speech recognition, they all belong to low-resource languages and encounter similar challenges. The purpose of this paper was to summarize the speech recognition technologies developed for these three languages, with the hope to identify critical problems and effective methods shared among all three languages. Meanwhile, by comparing the technical development pathways of the three languages, we hope to discover important factors for developing low-resource language speech recognition technology. We first reviewed the technologies that are commonly used to tackle the low-resource language ASR problem and, then, discussed the commonalities and individualities of Uyghur, Kazakh, and Kyrgyz. After that, we summarized the development history and presented the status of speech recognition technologies for the three languages. Through the discussion, we firstly found that the technical development of these three languages is rather unbalanced. The research progress of Uyghur has been going on for nearly 30 years, while the research on Kyrgyz started only in the last five years. Importantly, regardless of how much sooner or later the research started, rapid progress is always accompanied by the emergence of open-source data. Secondly, we also observed that the current progress in the three languages is largely attributed to the technology migration from common languages, rather than the \"native research\" on the target language. On the one hand, this indicates that human languages are similar in pronunciation, and this similarity can be utilized to build a strong ASR system even for languages with very limited resources. On the other hand, this indicates that the current research has paid little attention to languages' individualities. Thirdly, although the performance of speech recognition on the three languages has greatly improved, we should notice the limitation of the current benchmarks: most of the datasets are comprised of reading speech with low noise. The next technical breakthrough should be accompanied by the emergence of more complex and spontaneous datasets. Fourthly, there is little research on the uncertainty of the language itself. This uncertainty may be caused by many factors, such as the lack of standardization of the language itself, pronunciation discrepancy in different regions, linguistic changes influenced by word borrowing and general interference from other languages, and so on. Actually, since our focus in the paper was the three languages in China, the observations could be different from those found for the same languages in the middle of Asia. For example, the language description of Kazakh we presented in Appendix B is quite different from recent accounts by other authors [167; 168], although both were carefully designed and checked by linguists. Besides, for Uyghur, Kazakh, and Kyrgyz, based on the one-to-one letter-to-phoneme mapping, uncertainty in pronunciation will be reflected in the writing system, leading to noise in the vocabularies and language models. It is worth noting that this uncertainty is a common problem of many low-resource languages, but little research has been conducted in this direction. In summary, we think that low-resource language speech recognition has three main difficulties: data scarcity, uncertainty in pronunciation and writing, and language individuality. Currently, most studies focus on solving the data scarcity problem, includingmore data collection, transfer learning, pre-training, etc. These techniques are obviously of great significance. However, research on the uncertainty and uniqueness of languages is still limited.