Diffusion Conditional Expectation Model for Efficient and Robust Target
  Speech Extraction
Leying Zhang Yao Qian Linfeng Yu Heming Wang Xinkai Wang Hemin Yang Long Zhou Shujie Liu Yanmin Qian Michael Zeng
Target Speech Extraction (TSE) is a crucial task in speech processing that
focuses on isolating the clean speech of a specific speaker from complex
mixtures. While discriminative methods are commonly used for TSE, they can
introduce distortion in terms of speech perception quality. On the other hand,
generative approaches, particularly diffusion-based methods, can enhance speech
quality perceptually but suffer from slower inference speed. We propose an
efficient generative approach named Diffusion Conditional Expectation Model
(DCEM) for TSE. It can handle multi- and single-speaker scenarios in both noisy
and clean conditions. Additionally, we introduce Regenerate-DCEM (R-DCEM) that
can regenerate and optimize speech quality based on pre-processed speech from a
discriminative model. Our method outperforms conventional methods in terms of
both intrusive and non-intrusive metrics and demonstrates notable strengths in
inference efficiency and robustness to unseen tasks. Audio examples are
available online (https://vivian556123.github.io/dcem).