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This paper proposes an unsupervised data selection method by using a submodular function based on contrastive loss ratios of target and training data sets. A model using a contrastive loss function is trained on both sets. Then the ratio of frame-level losses for each model is used by a submodular function. By using the submodular function, a training set for automatic speech recognition matching the target data set is selected.

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In recent years, audio and video deepfake technology has advanced relentlessly, severely impacting people's reputation and reliability.
Several factors have facilitated the growing deepfake threat.
On the one hand, the hyper-connected society of social and mass media enables the spread of multimedia content worldwide in real-time, facilitating the dissemination of counterfeit material.

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Feature selection has been explored in two ways, global feature selection and instance-wise feature selection. Global feature selection picks the same feature selector for the entire dataset, while instance-wise feature selection allows different feature selectors for different data instances. We propose group-wise feature selection, a new setting that sits between global and instance-wise feature selections.

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While end-to-end models have shown great success on the Automatic Speech Recognition task, performance degrades severely when target sentences are long-form. The previous proposed methods, (partial) overlapping inference are shown to be effective on long-form decoding. For both methods, word error rate (WER) decreases monotonically when over- lapping percentage decreases. Setting aside computational cost, the setup with 50% overlapping during inference can achieve the best performance. However, a lower overlapping percentage has an advantage of fast inference speed.

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