  The Audio Deep Synthesis Detection (ADD) Challenge has been held to detect
generated human-like speech. With our submitted system, this paper provides an
overall assessment of track 1 (Low-quality Fake Audio Detection) and track 2
(Partially Fake Audio Detection). In this paper, spectro-temporal artifacts
were detected using raw temporal signals, spectral features, as well as deep
embedding features. To address track 1, low-quality data augmentation, domain
adaptation via finetuning, and various complementary feature information fusion
were aggregated in our system. Furthermore, we analyzed the clustering
characteristics of subsystems with different features by visualization method
and explained the effectiveness of our proposed greedy fusion strategy. As for
track 2, frame transition and smoothing were detected using self-supervised
learning structure to capture the manipulation of PF attacks in the time
domain. We ranked 4th and 5th in track 1 and track 2, respectively.
