Exploiting cross-lingual resources is an effective way to compensate for data scarcity of low resource languages. Recently, a novel multilingual model fusion technique has been proposed where a model is trained to learn cross-lingual acoustic-phonetic similarities as a mapping function. However, handcrafted lexicons have been used to train hybrid DNN-HMM ASR systems. To remove this dependency, we extend the concept of learnable cross-lingual mappings for end-to-end speech recognition. Furthermore, mapping models are employed to transliterate the source languages to the target language without using parallel data. Finally, the source audio and its transliteration is used for data augmentation to retrain the target language ASR. The results show that any source language ASR model can be used for a low-resource target language recognition followed by proposed mapping model. Furthermore, data augmentation results in a relative gain up to 5% over baseline monolingual model.

翻译：利用跨语言资源是弥补低资源语言数据稀缺的有效途径。近期，一种新颖的多语言模型融合技术被提出，该技术通过训练模型学习跨语言声学-语音相似性作为映射函数。然而，混合DNN-HMM自动语音识别系统的训练依赖手工构建的词典。为消除这一依赖，我们将可学习的跨语言映射概念扩展至端到端语音识别领域。进一步地，映射模型被用于将源语言音译为目标语言，且无需借助平行语料。最后，利用源语言音频及其音译结果进行数据增强，以重新训练目标语言的自动语音识别模型。实验结果表明，结合提出的映射模型，任意源语言自动语音识别模型均可用于低资源目标语言识别。此外，与基线单语模型相比，数据增强带来了最高5%的相对性能增益。