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Research on Tibetan Speech Recognition Based on the Am-do Dialect

Kuntharrgyal Khysru1,*, Jianguo Wei1,2, Jianwu Dang3

1 Key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission, Qinghai Minzu University, Xining, 810007, China
2 Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300072, China
3 Japan Advanced Institute of Science and Technology, Ishikawa, Japan

* Corresponding Author: Kuntharrgyal Khysru. Email:

Computers, Materials & Continua 2022, 73(3), 4897-4907.


In China, Tibetan is usually divided into three major dialects: the Am-do, Khams and Lhasa dialects. The Am-do dialect evolved from ancient Tibetan and is a local variant of modern Tibetan. Although this dialect has its own specific historical and social conditions and development, there have been different degrees of communication with other ethnic groups, but all the abovementioned dialects developed from the same language: Tibetan. This paper uses the particularity of Tibetan suffixes in pronunciation and proposes a lexicon for the Am-do language, which optimizes the problems existing in previous research. Audio data of the Am-do dialect are expanded by data augmentation technology combining noise and reverberation, and the morphological characteristics and characteristics of the Tibetan language are further considered. According to the particularity of Tibetan grammar, grammatical features are used to optimize grammatical relationships and are combined with a language model, and the Am-do dialect is scored and rescored. Experimental results show that compared with the baseline, our proposed new lexicon and data augmentation technology yields a relative increase of approximately 3% in character error rates (CERs) and a relative increase of 3%–19% in the recognition rate of acoustic models and language models.


Cite This Article

K. Khysru, J. Wei and J. Dang, "Research on tibetan speech recognition based on the am-do dialect," Computers, Materials & Continua, vol. 73, no.3, pp. 4897–4907, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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