  Despite significant progress made in the last decade, deep neural network
(DNN) based speech enhancement (SE) still faces the challenge of notable
degradation in the quality of recovered speech under low signal-to-noise ratio
(SNR) conditions. In this letter, we propose an SNR-progressive speech
enhancement model with harmonic compensation for low-SNR SE. Reliable pitch
estimation is obtained from the intermediate output, which has the benefit of
retaining more speech components than the coarse estimate while possessing a
significant higher SNR than the input noisy speech. An effective harmonic
compensation mechanism is introduced for better harmonic recovery. Extensive
ex-periments demonstrate the advantage of our proposed model. A multi-modal
speech extraction system based on the proposed backbone model ranks first in
the ICASSP 2024 MISP Challenge:
https://mispchallenge.github.io/mispchallenge2023/index.html.
