# How to Leverage DNN-based speech enhancement for multi-channel speaker verification?

arXiv: https://arxiv.org/abs/2210.08834 · HTML: https://mlsanity.com/paper/2210.08834/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2022.

## Built on by (most cited first, 2 of 2)

- [Speaker Embeddings to Improve Tracking of Intermittent and Moving Speakers](https://mlsanity.com/paper/2506.19875/) (2025)
- [Joint Beamforming and Speaker-Attributed ASR for Real Distant-Microphone Meeting Transcription](https://mlsanity.com/paper/2410.21849/) (2024)

## Abstract (arXiv)

Speaker verification (SV) suffers from unsatisfactory performance in far-field scenarios due to environmental noise andthe adverse impact of room reverberation. This work presents a benchmark of multichannel speech enhancement for far-fieldspeaker verification. One approach is a deep neural network-based, and the other is a combination of deep neural network andsignal processing. We integrated a DNN architecture with signal processing techniques to carry out various experiments. Ourapproach is compared to the existing state-of-the-art approaches. We examine the importance of enrollment in pre-processing,which has been largely overlooked in previous studies. Experimental evaluation shows that pre-processing can improve the SVperformance as long as the enrollment files are processed similarly to the test data and that test and enrollment occur within similarSNR ranges. Considerable improvement is obtained on the generated and all the noise conditions of the VOiCES dataset.