Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning
CoRR(2023)
摘要
In this paper, we explore a continuous modeling approach for
deep-learning-based speech enhancement, focusing on the denoising process. We
use a state variable to indicate the denoising process. The starting state is
noisy speech and the ending state is clean speech. The noise component in the
state variable decreases with the change of the state index until the noise
component is 0. During training, a UNet-like neural network learns to estimate
every state variable sampled from the continuous denoising process. In testing,
we introduce a controlling factor as an embedding, ranging from zero to one, to
the neural network, allowing us to control the level of noise reduction. This
approach enables controllable speech enhancement and is adaptable to various
application scenarios. Experimental results indicate that preserving a small
amount of noise in the clean target benefits speech enhancement, as evidenced
by improvements in both objective speech measures and automatic speech
recognition performance.
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关键词
speech enhancement
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