# 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
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Resource** & **Language** & **Date** & **Contributor** & **Data Size** & **Attributes** \\ \hline KSC [114]\({}^{5}\) & Kazakh & 2020 & ISSAI & Speech: 332 h & Crowdsourced \\ \hline KazakhTTS [115]\({}^{6}\) & Kazakh & 2021 & ISSAI & Speech: 93 h & 2 Speakers \\ \hline KazakhTTS2 [116]\({}^{6}\) & Kazakh & 2022 & ISSAI & Speech: 271 h & 5 Speakers \\ \hline KSC2 [117]\({}^{7}\) & Kazakh & 2022 & ISSAI & Speech: 1128 h &
\begin{tabular}{l} Crowdsourced, \\ Reading \\ \end{tabular} \\ \hline \multicolumn{6}{l}{\({}^{1}\) [[http://openslr.org/22](http://openslr.org/22)]([http://openslr.org/22](http://openslr.org/22)) (Free). \({}^{2}\) [http://m2 asr.cslt.org](http://m2 asr.cslt.org) (Free). \({}^{3}\) [[https://commonvoice.mozilla.org/en/datasets](https://commonvoice.mozilla.org/en/datasets)]([https://commonvoice.mozilla.org/en/datasets](https://commonvoice.mozilla.org/en/datasets))} \\ \multicolumn{6}{l}{(Free). \({}^{4}\) [[https://catalog.ldc.upenn.edu/LDC2018S13](https://catalog.ldc.upenn.edu/LDC2018S13)]([https://catalog.ldc.upenn.edu/LDC2018S13](https://catalog.ldc.upenn.edu/LDC2018S13)) (Not free). \({}^{5}\) [[https://issai.nu.edu.kz/kz-speech-corpus](https://issai.nu.edu.kz/kz-speech-corpus)]([https://issai.nu.edu.kz/kz-speech-corpus](https://issai.nu.edu.kz/kz-speech-corpus))} \\ \multicolumn{6}{l}{(Free). \({}^{6}\) [[https://github.com/IS2](https://github.com/IS2) AI/K]([https://github.com/IS2](https://github.com/IS2) AI/K) Kazakh_TTS (Free). \({}^{7}\) [[https://github.com/IS2](https://github.com/IS2) AI/K]([https://github.com/IS2](https://github.com/IS2) AI/K) Kazakh_ASR (Free). All the resources were accessed on 25 December 2022.} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._lingual morpheme analysis tool, which can discover frequent morphemes from a list of words. This tool supports Uyghur, Kazakh, and Kyrgyz. [PERSON] et al. adopted this tool to build a morpheme-based Uyghur system and found that using morpheme-based LM can achieve better performance than word-based LMs [112]. In 2018, [PERSON] et al. [133] proposed a subword-word end-to-end ASR model and reported good performance on a private Uyghur database involving 1000 h of speech. Further research showed that combining subword units and BPE encoding usually offered good performance in Uyghur ASR [134; 135]. It is worth noting that most of the above studies were based on private data. An obvious consequence is that the claimed experimental results cannot be reproduced, making the research unverifiable. In addition to the inaccessible private database, the lack of open-source code is another obstacle for research development. This makes it difficult for readers to reproduce the blend of techniques, thus being unable to verify their effectiveness and innovation. To solve these two problems, a standard benchmark database and its accompanying baselines are required. In 2017, [PERSON] et al. [136] described how they constructed a database of 1000 h of Uyghur, but neither the source code, nor the collected data were published. In the same year, Tsinghua University and Xinjiang University published a free Uyghur database, THUYG-20 [112], and also released several baselines with popular techniques, making technology comparison and discussion possible. Since then, many researchers have reported substantial progress based on this open database [127; 129; 135; 136; 137; 138; 139]. Table 4 shows the performance of different technologies reported on THUYG-20, from which we can clearly see the roadmap of technical progress.