  We propose DiffSpEx, a generative target speaker extraction method based on
score-based generative modelling through stochastic differential equations.
DiffSpEx deploys a continuous-time stochastic diffusion process in the complex
short-time Fourier transform domain, starting from the target speaker source
and converging to a Gaussian distribution centred on the mixture of sources.
For the reverse-time process, a parametrised score function is conditioned on a
target speaker embedding to extract the target speaker from the mixture of
sources. We utilise ECAPA-TDNN target speaker embeddings and condition the
score function alternately on the SDE time embedding and the target speaker
embedding. The potential of DiffSpEx is demonstrated with the WSJ0-2mix
dataset, achieving an SI-SDR of 12.9 dB and a NISQA score of 3.56. Moreover, we
show that fine-tuning a pre-trained DiffSpEx model to a specific speaker
further improves performance, enabling personalisation in target speaker
extraction.
