Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition

Qianying Liu; Zhuo Gong; Zhengdong Yang; Yuhang Yang; Sheng Li; Chenchen Ding; Nobuaki Minematsu; Hao Huang; Fei Cheng; Chenhui Chu; Sadao Kurohashi

arXiv · 2204.03855

Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition

Qianying Liu·Zhuo Gong·Zhengdong Yang·Yuhang Yang·Sheng Li·Chenchen Ding·Nobuaki Minematsu·Hao Huang·Fei Cheng·Chenhui Chu·Sadao Kurohashi

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Abstract

Low-resource speech recognition has been long-suffering from insufficient training data. In this paper, we propose an approach that leverages neighboring languages to improve low-resource scenario performance, founded on the hypothesis that similar linguistic units in neighboring languages exhibit comparable term frequency distributions, which enables us to construct a Huffman tree for performing multilingual hierarchical Softmax decoding. This hierarchical structure enables cross-lingual knowledge sharing among similar tokens, thereby enhancing low-resource training outcomes. Empirical analyses demonstrate that our method is effective in improving the accuracy and efficiency of low-resource speech recognition.

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Qianying Liu, Zhuo Gong, Zhengdong Yang, Yuhang Yang, Sheng Li, Chenchen Ding, Nobuaki Minematsu, Hao Huang, Fei Cheng, Chenhui Chu, Sadao Kurohashi. 2022-04-08. Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition. https://arxiv.org/abs/2204.03855

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