# Impact of Microphone Array Mismatches to Learning-based Replay Speech Detection

arXiv: https://arxiv.org/abs/2503.07357 · HTML: https://mlsanity.com/paper/2503.07357/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [Multi-Channel Replay Speech Detection using Acoustic Maps](https://mlsanity.com/paper/2602.16399/) (2026)

## Abstract (arXiv)

In this work, we investigate the generalization of a multi-channel learning-based replay speech detector, which employs adaptive beamforming and detection, across different microphone arrays. In general, deep neural network-based microphone array processing techniques generalize poorly to unseen array types, i.e., showing a significant training-test mismatch of performance. We employ the ReMASC dataset to analyze performance degradation due to inter- and intra-device mismatches, assessing both single- and multi-channel configurations. Furthermore, we explore fine-tuning to mitigate the performance loss when transitioning to unseen microphone arrays. Our findings reveal that array mismatches significantly decrease detection accuracy, with intra-device generalization being more robust than inter-device. However, fine-tuning with as little as ten minutes of target data can effectively recover performance, providing insights for practical deployment of replay detection systems in heterogeneous automatic speaker verification environments.