Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition
This addresses the problem of insufficient training data for low-resource speech recognition, but it appears incremental as it builds on existing multilingual and hierarchical approaches.
The paper tackles low-resource speech recognition by leveraging neighboring languages to share knowledge through a hierarchical Softmax decoding method, resulting in improved accuracy and efficiency.
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.