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pro vyhledávání: '"Kim, Jin Sob"'
Recent advancements in automatic speaker verification (ASV) studies have been achieved by leveraging large-scale pretrained networks. In this study, we analyze the approaches toward such a paradigm and underline the significance of interlayer informa
Externí odkaz:
http://arxiv.org/abs/2409.07770
Expressive Text-to-Speech (TTS) using reference speech has been studied extensively to synthesize natural speech, but there are limitations to obtaining well-represented styles and improving model generalization ability. In this study, we present Dif
Externí odkaz:
http://arxiv.org/abs/2406.19135
In semi-supervised semantic segmentation, the Mean Teacher- and co-training-based approaches are employed to mitigate confirmation bias and coupling problems. However, despite their high performance, these approaches frequently involve complex traini
Externí odkaz:
http://arxiv.org/abs/2405.20610
Sound event localization and detection (SELD) combines the identification of sound events with the corresponding directions of arrival (DOA). Recently, event-oriented track output formats have been adopted to solve this problem; however, they still h
Externí odkaz:
http://arxiv.org/abs/2303.15703
Voice Conversion (VC) must be achieved while maintaining the content of the source speech and representing the characteristics of the target speaker. The existing methods do not simultaneously satisfy the above two aspects of VC, and their conversion
Externí odkaz:
http://arxiv.org/abs/2303.09057
Recent deep learning models have achieved high performance in speech enhancement; however, it is still challenging to obtain a fast and low-complexity model without significant performance degradation. Previous knowledge distillation studies on speec
Externí odkaz:
http://arxiv.org/abs/2208.10367
In the field of speech enhancement, time domain methods have difficulties in achieving both high performance and efficiency. Recently, dual-path models have been adopted to represent long sequential features, but they still have limited representatio
Externí odkaz:
http://arxiv.org/abs/2203.02181
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