Spatial-temporal Graph Based Multi-channel Speaker Verification With Ad-hoc Microphone Arrays

TL;DR

This paper introduces a spatial-temporal graph convolutional network for multi-channel speaker verification using ad-hoc microphone arrays, effectively reducing noise impact and improving accuracy in challenging acoustic environments.

Contribution

It presents a novel graph-based framework with feature aggregation and channel selection blocks for enhanced multi-channel speaker verification.

Findings

Achieves up to 17.70% relative EER reduction in real environments

Robust performance across various noise and reverberation conditions

Outperforms six baseline methods in experiments

Abstract

The performance of speaker verification degrades significantly in adverse acoustic environments with strong reverberation and noise. To address this issue, this paper proposes a spatial-temporal graph convolutional network (GCN) method for the multi-channel speaker verification with ad-hoc microphone arrays. It includes a feature aggregation block and a channel selection block, both of which are built on graphs. The feature aggregation block fuses speaker features among different time and channels by a spatial-temporal GCN. The graph-based channel selection block discards the noisy channels that may contribute negatively to the system. The proposed method is flexible in incorporating various kinds of graphs and prior knowledge. We compared the proposed method with six representative methods in both real-world and simulated environments. Experimental results show that the proposed method achieves a relative equal error rate (EER) reduction of $\mathbf{15.39\%}$ lower than the strongest referenced method in the simulated datasets, and $\mathbf{17.70\%}$ lower than the latter in the real datasets. Moreover, its performance is robust across different signal-to-noise ratios and reverberation time.