  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.
