# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 1 Introduction  
According to the statistics of the Ethnologue website ([[https://www.ethnologue.com/](https://www.ethnologue.com/)]([https://www.ethnologue.com/](https://www.ethnologue.com/)), accessed on 25 December 2022), there are 7151 known living languages in the world (every living language has at least one speaker for whom it is his/her first language). Most of these are low-resource languages, which refers to a language with some (if not all) of the following aspects [1]: a lack of a unique writing system or a stable orthography, a limited presence on the web, a lack of linguistic expertise, and/or a lack of electronic resources for speech and language processing, such as monolingual corpora, bilingual dictionaries, transcribed speech data, pronunciation dictionaries, vocabularies, etc.  
Information-processing technology for low-resource languages is of great significance to strengthen culture exchanges, promote information fairness, and protect endangered languages. Automatic speech recognition (ASR) is among the important technologies in language information processing. After several decades of development, modern ASR systems, especially for rich-resource languages, have achieved significant performance improvements and have been deployed in a wide range of practical applications. However, the performances for low-resource languages are still far from satisfactory.  
In general, ASR for low-resource languages encounters three challenges: (1) the data scarcity of speech and text, leading to difficult model training; (2) a lack of standardization, a discrepancy in the pronunciation, and linguistic changes, leading to the uncertainty of thelanguages. (3) each language having its own individual properties, reflected in the writing, linguistic, phonetic, acoustic, and other aspects. If these challenges cannot be solved well, it would be impossible to achieve reasonable recognition performance. Currently, the vast majority of research focuses on the data scarcity, while less attention has been paid to the standardization and uniqueness of the language itself.  
Uyghur, Kazakh, and Kyrgyz are all low-resource languages and possess the challenges mentioned above. However, they all belong to the Altaic branch of the Altaic language family, and so, they have a large degree of similarity at both the acoustic and linguistic levels. Especially in China, the three languages are distributed across neighboring regions, which closely interact with each other, so they are quite similar in their writing, pronunciation, and syntax [2]. From the perspective of ASR, this means that similar techniques, especially those that can be used to tackle the challenges of low-resource languages, can be used for all three.  
Therefore, this paper investigates these three languages together and summarizes the modern algorithms/schemes for each language. Our first goal was to highlight the important techniques that have been broadly verified so that researchers working on different languages can learn from each other. In addition, by comparing the technical roadmap of these three neighboring languages, we hope to discover the key factors for developing low-research language speech recognition research, thereby providing some suggestions on the \"important work\" that researchers may focus on.  
The rest of this paper is organized as follows: Section 2 presents an overview of low-resource language speech recognition methods. Section 3 provides a comprehensive linguistic analysis of Uyghur, Kazakh, and Kyrgyz and summarizes their commonalities and individualities. A technical review, analysis, and discussion of the speech recognition techniques for these three languages are reported in Section 4. Section 5 concludes the paper.