  Neural beamformers, which integrate both pre-separation and beamforming
modules, have demonstrated impressive effectiveness in target speech
extraction. Nevertheless, the performance of these beamformers is inherently
limited by the predictive accuracy of the pre-separation module. In this paper,
we introduce a neural beamformer supported by a dual-path transformer.
Initially, we employ the cross-attention mechanism in the time domain to
extract crucial spatial information related to beamforming from the noisy
covariance matrix. Subsequently, in the frequency domain, the self-attention
mechanism is employed to enhance the model's ability to process
frequency-specific details. By design, our model circumvents the influence of
pre-separation modules, delivering performance in a more comprehensive
end-to-end manner. Experimental results reveal that our model not only
outperforms contemporary leading neural beamforming algorithms in separation
performance but also achieves this with a significant reduction in parameter
count.
