Dual-path Transformer Based Neural Beamformer for Target Speech Extraction
Abstract
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.
Cite
@article{arxiv.2308.15990,
title = {Dual-path Transformer Based Neural Beamformer for Target Speech Extraction},
author = {Aoqi Guo and Sichong Qian and Baoxiang Li and Dazhi Gao},
journal= {arXiv preprint arXiv:2308.15990},
year = {2023}
}