Skip to Main content Skip to Navigation
Conference papers

A Comprehensive Exploration of Noise Robustness and Noise Compensation in ResNet and TDNN-based Speaker Recognition Systems

Mohammad Mohammadamini 1 Driss Matrouf 1 Jean-François Bonastre 1 Sandipana Dowerah 2 Romain Serizel 2 Denis Jouvet 2 
2 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this paper, a comprehensive exploration of noise robustness and noise compensation of ResNet and TDNN speaker recognition systems is presented. Firstly the robustness of the TDNN and ResNet in the presence of noise, reverberation, and both distortions is explored. Our experimental results show that in all cases the ResNet system is more robust than TDNN. After that, a noise compensation task is done with denoising autoencoder (DAE) over the x-vectors extracted from both systems. We explored two scenarios: 1) compensation of artificial noise with artificial data, 2) compensation of real noise with artificial data. The second case is the most desired scenario, because it makes noise compensation affordable without having real data to train denoising techniques. The experimental results show that in the first scenario noise compensation gives significant improvement with TDNN while this improvement in Resnet is not significant. In the second scenario, we achieved 15% improvement of EER over VoiCes Eval challenge in both TDNN and ResNet systems. In most cases the performance of ResNet without compensation is superior to TDNN with noise compensation.
Document type :
Conference papers
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03669919
Contributor : Mohammad Mohammadamini Connect in order to contact the contributor
Submitted on : Wednesday, May 25, 2022 - 7:49:09 PM
Last modification on : Tuesday, May 31, 2022 - 3:38:59 AM

File

A Comprehensive Exploration of...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03669919, version 1

Citation

Mohammad Mohammadamini, Driss Matrouf, Jean-François Bonastre, Sandipana Dowerah, Romain Serizel, et al.. A Comprehensive Exploration of Noise Robustness and Noise Compensation in ResNet and TDNN-based Speaker Recognition Systems. EUSIPCO 2022 - 30th European Signal Processing Conference, Aug 2022, Belgrade, Serbia. ⟨hal-03669919⟩

Share

Metrics

Record views

0

Files downloads

0