Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
With the emergence of deep learning, the performance of automatic speech recognition (ASR) systems has remarkably improved. Especially for resource-rich languages such as English and Chinese, commercial usage has been made feasible in a wide range of applications. However, most languages are low-res...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/1/326 |
| _version_ | 1797626242233532416 |
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| author | Wenqiang Du Yikeremu Maimaitiyiming Mewlude Nijat Lantian Li Askar Hamdulla Dong Wang |
| author_facet | Wenqiang Du Yikeremu Maimaitiyiming Mewlude Nijat Lantian Li Askar Hamdulla Dong Wang |
| author_sort | Wenqiang Du |
| collection | DOAJ |
| description | With the emergence of deep learning, the performance of automatic speech recognition (ASR) systems has remarkably improved. Especially for resource-rich languages such as English and Chinese, commercial usage has been made feasible in a wide range of applications. However, most languages are low-resource languages, presenting three main difficulties for the development of ASR systems: (1) the scarcity of the data; (2) the uncertainty in the writing and pronunciation; (3) the individuality of each language. Uyghur, Kazakh, and Kyrgyz as examples are all low-resource languages, involving clear geographical variation in their pronunciation, and each language possesses its own unique acoustic properties and phonological rules. On the other hand, they all belong to the Altaic language family of the Altaic branch, so they share many commonalities. This paper presents an overview of speech recognition techniques developed for Uyghur, Kazakh, and Kyrgyz, with the purposes of (1) highlighting the techniques that are specifically effective for each language and generally effective for all of them and (2) discovering the important factors in promoting the speech recognition research of low-resource languages, by a comparative study of the development path of these three neighboring languages. |
| first_indexed | 2024-03-11T10:07:36Z |
| format | Article |
| id | doaj.art-29d074a84ae54674a6bdd3a6ab94ee89 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T10:07:36Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-29d074a84ae54674a6bdd3a6ab94ee892023-11-16T14:55:29ZengMDPI AGApplied Sciences2076-34172022-12-0113132610.3390/app13010326Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An OverviewWenqiang Du0Yikeremu Maimaitiyiming1Mewlude Nijat2Lantian Li3Askar Hamdulla4Dong Wang5Center for Speech and Language Technologies, BNRist, Tsinghua University, Beijing 100084, ChinaSchool of Information Science and Engineering, Xinjiang University, Ürümqi 830017, ChinaSchool of Information Science and Engineering, Xinjiang University, Ürümqi 830017, ChinaSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information Science and Engineering, Xinjiang University, Ürümqi 830017, ChinaCenter for Speech and Language Technologies, BNRist, Tsinghua University, Beijing 100084, ChinaWith the emergence of deep learning, the performance of automatic speech recognition (ASR) systems has remarkably improved. Especially for resource-rich languages such as English and Chinese, commercial usage has been made feasible in a wide range of applications. However, most languages are low-resource languages, presenting three main difficulties for the development of ASR systems: (1) the scarcity of the data; (2) the uncertainty in the writing and pronunciation; (3) the individuality of each language. Uyghur, Kazakh, and Kyrgyz as examples are all low-resource languages, involving clear geographical variation in their pronunciation, and each language possesses its own unique acoustic properties and phonological rules. On the other hand, they all belong to the Altaic language family of the Altaic branch, so they share many commonalities. This paper presents an overview of speech recognition techniques developed for Uyghur, Kazakh, and Kyrgyz, with the purposes of (1) highlighting the techniques that are specifically effective for each language and generally effective for all of them and (2) discovering the important factors in promoting the speech recognition research of low-resource languages, by a comparative study of the development path of these three neighboring languages.https://www.mdpi.com/2076-3417/13/1/326overviewautomatic speech recognitionlow-resourceUyghurKazakhKyrgyz |
| spellingShingle | Wenqiang Du Yikeremu Maimaitiyiming Mewlude Nijat Lantian Li Askar Hamdulla Dong Wang Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview Applied Sciences overview automatic speech recognition low-resource Uyghur Kazakh Kyrgyz |
| title | Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview |
| title_full | Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview |
| title_fullStr | Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview |
| title_full_unstemmed | Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview |
| title_short | Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview |
| title_sort | automatic speech recognition for uyghur kazakh and kyrgyz an overview |
| topic | overview automatic speech recognition low-resource Uyghur Kazakh Kyrgyz |
| url | https://www.mdpi.com/2076-3417/13/1/326 |
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