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

Abstract

Speaker verification (SV) suffers from unsatisfactory performance in far-field scenarios due to environmental noise and the adverse impact of room reverberation. This work presents a benchmark of multichannel speech enhancement for far-field speaker verification. One approach is a deep neural network-based, and the other is a combination of deep neural network and signal processing. We integrated a DNN architecture with signal processing techniques to carry out various experiments. Our approach 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 SV performance as long as the enrollment files are processed similarly to the test data and that test and enrollment occur within similar SNR ranges. Considerable improvement is obtained on the generated and all the noise conditions of the VOiCES dataset.
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Dates and versions

hal-03619903 , version 1 (25-03-2022)
hal-03619903 , version 2 (14-10-2022)

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Sandipana Dowerah, Romain Serizel, Denis Jouvet, Mohammad Mohammadamini, Driss Matrouf. How to Leverage DNN-based speech enhancement for multi-channel speaker verification?. 4th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2022), Oct 2022, Corfu, Greece. ⟨hal-03619903v2⟩
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