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Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation ...
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Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation ...
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ConSLT: A Token-level Contrastive Framework for Sign Language Translation ...
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A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation ...
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Focus on the Target's Vocabulary: Masked Label Smoothing for Machine Translation ...
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USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition ...
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Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning ...
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GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding ...
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Delving Deeper into Cross-lingual Visual Question Answering ...
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Multi-Level Contrastive Learning for Cross-Lingual Alignment ...
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Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training ...
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HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity ...
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Controllable Natural Language Generation with Contrastive Prefixes ...
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SGL: Symbolic Goal Learning in a Hybrid, Modular Framework for Human Instruction Following ...
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Local-Global Context Aware Transformer for Language-Guided Video Segmentation ...
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Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset ...
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Yu, Tiezheng; Frieske, Rita; Xu, Peng; Cahyawijaya, Samuel; Yiu, Cheuk Tung Shadow; Lovenia, Holy; Dai, Wenliang; Barezi, Elham J.; Chen, Qifeng; Ma, Xiaojuan; Shi, Bertram E.; Fung, Pascale. - : arXiv, 2022
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Abstract:
Automatic speech recognition (ASR) on low resource languages improves the access of linguistic minorities to technological advantages provided by artificial intelligence (AI). In this paper, we address the problem of data scarcity for the Hong Kong Cantonese language by creating a new Cantonese dataset. Our dataset, Multi-Domain Cantonese Corpus (MDCC), consists of 73.6 hours of clean read speech paired with transcripts, collected from Cantonese audiobooks from Hong Kong. It comprises philosophy, politics, education, culture, lifestyle and family domains, covering a wide range of topics. We also review all existing Cantonese datasets and analyze them according to their speech type, data source, total size and availability. We further conduct experiments with Fairseq S2T Transformer, a state-of-the-art ASR model, on the biggest existing dataset, Common Voice zh-HK, and our proposed MDCC, and the results show the effectiveness of our dataset. In addition, we create a powerful and robust Cantonese ASR model by ...
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Keyword:
Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
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URL: https://dx.doi.org/10.48550/arxiv.2201.02419 https://arxiv.org/abs/2201.02419
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