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2021 IEEE GRSS Data Fusion Contest: Track MSD Multitemporal Semantic Change Detection The Challenge Track he multitemporal semantic change detection challenge track (Track MSD) of the 2021 IEEE GRSS Data […]

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CCA-MDD: A Coupled Cross-Attention based Framework for Streaming Mispronunciation detection and diagnosis: Paper and Code. End-to-end models are becoming popular approaches for mispronunciation detection and diagnosis (MDD). A streaming MDD framework which is demanded by many practical applications still remains a challenge. This paper proposes a streaming end-to-end MDD framework called CCA-MDD. CCA-MDD supports online processing and is able to run strictly in real-time. The encoder of CCA-MDD consists of a conv-Transformer network based streaming acoustic encoder and an improved cross-attention named coupled cross-attention (CCA). The coupled cross-attention integrates encoded acoustic features with pre-encoded linguistic features. An ensemble of decoders trained from multi-task learning is applied for final MDD decision. Experiments on publicly available corpora demonstrate that CCA-MDD achieves comparable performance to published offline end-to-end MDD models.