  Multi-channel speech enhancement seeks to utilize spatial information to
distinguish target speech from interfering signals. While deep learning
approaches like the dual-path convolutional recurrent network (DPCRN) have made
strides, challenges persist in effectively modeling inter-channel correlations
and amalgamating multi-level information. In response, we introduce the
Parallel Dual-Path Convolutional Recurrent Network (PDPCRN). This acoustic
modeling architecture has two key innovations. First, a parallel design with
separate branches extracts complementary features. Second, bi-directional
modules enable cross-branch communication. Together, these facilitate diverse
representation fusion and enhanced modeling. Experimental validation on TIMIT
datasets underscores the prowess of PDPCRN. Notably, against baseline models
like the standard DPCRN, PDPCRN not only outperforms in PESQ and STOI metrics
but also boasts a leaner computational footprint with reduced parameters.
