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.

利用可学习的跨语言映射和数据增强来提高低资源语言的端到端语音识别准确度。

学习跨语言映射提升低资源语音识别的数据增强

Learning Cross-lingual Mappings for Data Augmentation to Improve  Low-Resource Speech Recognition

This paper contributes to cross-lingual image annotation and retrieval in
terms of data and baseline methods. We propose COCO-CN, a novel dataset
enriching MS-COCO with manually written Chinese sentences and tags. For more
effective annotation acquisition, we develop a recommendation-assisted
collective annotation system, automatically providing an annotator with several
tags and sentences deemed to be relevant with respect to the pictorial content.
Having 20,342 images annotated with 27,218 Chinese sentences and 70,993 tags,
COCO-CN is currently the largest Chinese-English dataset that provides a
unified and challenging platform for cross-lingual image tagging, captioning
and retrieval. We develop conceptually simple yet effective methods per task
for learning from cross-lingual resources. Extensive experiments on the three
tasks justify the viability of the proposed dataset and methods. Data and code
are publicly available at this https URL

本文提出了 COCO-CN 数据集和推荐辅助集体注释系统，通过学习跨语言资源，完成了跨语言的图片标记、字幕和检索任务，为跨语言图像注释和检索领域做出了重要贡献。