  Recently, more and more personalized speech enhancement systems (PSE) with
excellent performance have been proposed. However, two critical issues still
limit the performance and generalization ability of the model: 1) Acoustic
environment mismatch between the test noisy speech and target speaker
enrollment speech; 2) Hard sample mining and learning. In this paper, dynamic
acoustic compensation (DAC) is proposed to alleviate the environment mismatch,
by intercepting the noise or environmental acoustic segments from noisy speech
and mixing it with the clean enrollment speech. To well exploit the hard
samples in training data, we propose an adaptive focal training (AFT) strategy
by assigning adaptive loss weights to hard and non-hard samples during
training. A time-frequency multi-loss training is further introduced to improve
and generalize our previous work sDPCCN for PSE. The effectiveness of proposed
methods are examined on the DNS4 Challenge dataset. Results show that, the DAC
brings large improvements in terms of multiple evaluation metrics, and AFT
reduces the hard sample rate significantly and produces obvious MOS score
improvement.

Audio and Speech Processing