Inplace Cepstral Speech Enhancement System for the ICASSP 2023 Clarity Challenge
Jinjiang Liu, Xueliang Zhang · 2023
This report summarizes our system submission to the ICASSP 2023 Clarity Challenge. The goal of the challenge is to estimate clean binaural speech signals within a 5 ms system delay. Our submitted Inplace Cepstral Speech Enhancement (ICSE) system features the following aspects: First, we developed a low-latency short-time Fourier transform (LL-STFT) analysis and synthesis strategy for a neural network-based speech enhancement algorithm in the time-frequency domain. Second, we designed an end-to-end Inplace Cepstral Speech Enhancement neural network that achieves good spatial resolution in an inplace speech enhancement framework. We also combined the cepstrum space speech enhancement with the TF-domain speech enhancement in the proposed system. Finally, we employed a speech model-based perceptual loss to improve speech intelligibility and quality. The experimental results show that the proposed system significantly outperforms the baseline system and ranked among the top five systems.