MATEC Web Conf.
Volume 336, 20212020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|Number of page(s)||7|
|Section||Artificial Recognition and Application|
|Published online||15 February 2021|
- G. Yang, Y. Cuo. Research Status and Prospect of Tibetan Language Model [J]. Computer Knowledge and Technology. 16 3 (2020) [Google Scholar]
- S. Tongtong. Research on Tibetan Language Model Based on Recurrent Neural Network [D]. Tianjin University. 12 (2017) [Google Scholar]
- H. Zhaxi. Research on Tibetan Word Spell Checking Technology based on LSTM [D]. Qinghai Normal University. 3 (2020) [Google Scholar]
- Geoffrey E. Hinton, Simon Osindero, Yee-Whye Teh. A Fast Learning Algorithm for Deep Belief Nets [J]. Neural Computation. 7 (2006) [Google Scholar]
- Tjandra A, Sakti S, Nakamura S. End-to-end speech recognition sequence training with reinforcement learning [J]. IEEE Access. (2019) [Google Scholar]
- Rosenberg A, Audhkhasi K, Sethy A, et al.End-to-end speech recognition and keyword search on low-resource languages [C]. Speech and Signal Processing. 5280-5284. (2017) [Google Scholar]
- Zhao Yue, Yue Jianjian, Xu Xiaona, et al. End-to-end-based Tibetan multitask speech recognition. IEEE Access. (2019) [Google Scholar]
- Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need [C]. Advances in Neural Information Processing Systems. 5998-6008. (2017) [Google Scholar]
- W. Junhao, L. Yifeng, Enriching image descriptions by fusing fine-grained semantic features with a transformer [J]. Journal of East China Normal University. (2020) [Google Scholar]
- C. Zhuoma, C. Zhijie. An algorithm for word component decomposition in Tibetan character frequency statistics system [J]. Computer Engineering and Science. 33(3):159-162. 15. (2011) [Google Scholar]
- C. Zhijie. Research on Key Techniques of Tibetan Word Vector Representation [D]. Qinghai Normal University. (2018) [Google Scholar]
- W. Shuangcheng, The characteristics of the complex vowels in Amdo Tibetan [J]. National language. 3 (2004) [Google Scholar]
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