  Diffusion model-based speech enhancement has received increased attention
since it can generate very natural enhanced signals and generalizes well to
unseen conditions. Diffusion models have been explored for several sub-tasks of
speech enhancement, such as speech denoising, dereverberation, and source
separation. In this paper, we investigate their use for target speech
extraction (TSE), which consists of estimating the clean speech signal of a
target speaker in a mixture of multi-talkers. TSE is realized by conditioning
the extraction process on a clue identifying the target speaker. We show we can
realize TSE using a conditional diffusion model conditioned on the clue.
Besides, we introduce ensemble inference to reduce potential extraction errors
caused by the diffusion process. In experiments on Libri2mix corpus, we show
that the proposed diffusion model-based TSE combined with ensemble inference
outperforms a comparable TSE system trained discriminatively.
