论文标题
端到端低资源多语言语音识别的层次软件
Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition
论文作者
论文摘要
低资源的语音识别一直在训练数据不足。在本文中，我们提出了一种利用邻近语言来改善低资源场景性能的方法，该表现基于以下假设：相邻语言中类似的语言单元表现出可比的术语频率分布，这使我们能够构建一棵huffman树，用于执行多种字体的层次级别层次软性软体模式解码。这种层次结构可以使类似令牌之间的跨语性知识共享，从而增强低资源培训成果。经验分析表明，我们的方法可有效提高低资源语音识别的准确性和效率。
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.
