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In this paper, we explore a continuous modeling approach fordeep-learning-based speech enhancement, focusing on the denoising process. Weuse a state variable to indicate the denoising process. The starting state isnoisy speech and the ending state is clean speech. The noise component in thestate variable decreases with the change of the state index until the noisecomponent is 0. During training, a UNet-like neural network learns to estimateevery state variable sampled from the continuous denoising process. In testing,we introduce a controlling factor as an embedding, ranging from zero to one, tothe neural network, allowing us to control the level of noise reduction. Thisapproach enables controllable speech enhancement and is adaptable to variousapplication scenarios. Experimental results indicate that preserving a smallamount of noise in the clean target benefits speech enhancement, as evidencedby improvements in both objective speech measures and automatic speechrecognition performance.

Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning