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Personalized speech enhancement combining band-split RNN and speaker attentive module. (arXiv:2302.09953v2 [eess.AS] UPDATED)

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Target speaker information can be utilized in speech enhancement (SE) models
to more effectively extract the desired speech. Previous works introduce the
speaker embedding into speech enhancement models by means of concatenation or
affine transformation. In this paper, we propose a speaker attentive module to
calculate the attention scores between the speaker embedding and the
intermediate features, which are used to rescale the features. By merging this
module in the state-of-the-art SE model, we construct the personalized SE model
for ICASSP Signal Processing Grand Challenge: DNS Challenge 5 (2023). Our
system achieves a final score of 0.529 on the blind test set of track1 and
0.549 on track2.

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