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- W4386858477 abstract "In speech enhancement tasks, noise in speech signals may not only come from the external environment, but also from speech signals that are similar to the original speech, such as regional accents or emotional speech. However, most existing speech-enhanced GANs (SEGANs) utilize a single discriminator for the discrimination task. To improve upon existing single-discriminator GAN models, we propose a novel method that introduces two discriminators, with a new training data item for the second discriminator. This second discriminator is specifically designed to distinguish between clean speech signals and emotional speech signals, thereby reducing speech distortion and improving the quality of enhanced speech. The experimental results demonstrate the superior performance of our proposed method compared to the existing GAN model in speech enhancement, which validates the effectiveness and feasibility of our approach." @default.
- W4386858477 created "2023-09-20" @default.
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- W4386858477 date "2023-04-21" @default.
- W4386858477 modified "2023-09-27" @default.
- W4386858477 title "A new method for improving generative adversarial networks in speech enhancement" @default.
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- W4386858477 doi "https://doi.org/10.1109/icsp58490.2023.10248837" @default.
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