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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2022,3,31]]},"abstract":"<jats:p>Chinese dialects discrimination is a challenging natural language processing task due to scarce annotation resource. In this article, we develop a novel Chinese dialects discrimination framework with transfer learning and data augmentation (CDDTLDA) in order to overcome the shortage of resources. To be more specific, we first use a relatively larger Chinese dialects corpus to train a source-side automatic speech recognition (ASR) model. Then, we adopt a simple but effective data augmentation method (i.e., speed, pitch, and noise disturbance) to augment the target-side low-resource Chinese dialects, and fine-tune another target ASR model based on the previous source-side ASR model. Meanwhile, the potential common semantic features between source-side and target-side ASR models can be captured by using self-attention mechanism. Finally, we extract the hidden semantic representation in the target ASR model to conduct Chinese dialects discrimination. Our extensive experimental results demonstrate that our model significantly outperforms state-of-the-art methods on two benchmark Chinese dialects corpora.<\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3473499","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:44:30Z","timestamp":1635727470000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Low-Resource Language Discrimination toward Chinese Dialects with Transfer Learning and Data Augmentation"],"prefix":"10.1145","volume":"21","author":[{"given":"Fan","family":"Xu","sequence":"first","affiliation":[{"name":"Jiangxi Normal University, Nanchang, China"}]},{"given":"Yangjie","family":"Dan","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University, Nanchang, China"}]},{"given":"Keyu","family":"Yan","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University, Nanchang, China"}]},{"given":"Yong","family":"Ma","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University, Nanchang, China"}]},{"given":"Mingwen","family":"Wang","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University, Nanchang, China"}]}],"member":"320","published-online":{"date-parts":[[2021,10,31]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2019.2946480"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2019-2561"},{"key":"e_1_3_2_4_2","first-page":"15","volume-title":"Proceedings of the 3rd Workshop on NLP for Similar Languages, Varieties and Dialects","author":"\u00c7\u00f6ltekin \u00c7a\u011fr\u0131","year":"2016","unstructured":"\u00c7a\u011fr\u0131 \u00c7\u00f6ltekin and Taraka Rama. 2016. 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