Low resource speech recognition has been long-suffering from insufficient training data. While neighbour languages are often used as assistant training data, it would be difficult for the model to induct similar units (character, subword, etc.) across the languages. In this paper, we assume similar units in neighbour language share similar term frequency and form a Huffman tree to perform multi-lingual hierarchical Softmax decoding. During decoding, the hierarchical structure can benefit the training of low-resource languages. Experimental results show the effectiveness of our method.

翻译：低资源语音识别长期受培训数据不足的影响,虽然邻语经常被用作助理培训数据,但该模式很难在各语言中引入类似的单元(字符、子词等),在本文中,我们假定邻语中的类似单位具有类似的用词频率,并形成一棵Huffman树,以进行多种语言的软体分层解码。在解码过程中,等级结构有利于低资源语言的培训。实验结果显示了我们方法的有效性。