PAS: Partial Additive Speech Data Augmentation Method for Noise Robust Speaker Verification[link]
Wonbin Kim , Hyun-seo Shin, Ju-ho Kim, Jungwoo Heo, Chan-yeong Lim and Ha-Jin Yu
Abstract
Background noise reduces speech intelligibility
and quality, making speaker verification (SV) in
noisy environments a challenging task. To improve the noise robustness of SV systems, additive noise data augmentation method has been
commonly used. In this paper, we propose a new
additive noise method, partial additive speech
(PAS), which aims to train SV systems to be
less affected by noisy environments. The experimental results demonstrate that PAS outperforms
traditional additive noise in terms of equal error rates (EER), with relative improvements of
4.64% and 5.01% observed in SE-ResNet34 and
ECAPA-TDNN. We also show the effectiveness
of proposed method by analyzing attention modules and visualizing speaker embeddings.
PAS: Partial Additive Speech Data Augmentation Method for Noise Robust Speaker Verification[link]
Wonbin Kim , Hyun-seo Shin, Ju-ho Kim, Jungwoo Heo, Chan-yeong Lim and Ha-Jin Yu
Abstract
Background noise reduces speech intelligibility and quality, making speaker verification (SV) in noisy environments a challenging task. To improve the noise robustness of SV systems, additive noise data augmentation method has been commonly used. In this paper, we propose a new additive noise method, partial additive speech (PAS), which aims to train SV systems to be less affected by noisy environments. The experimental results demonstrate that PAS outperforms traditional additive noise in terms of equal error rates (EER), with relative improvements of 4.64% and 5.01% observed in SE-ResNet34 and ECAPA-TDNN. We also show the effectiveness of proposed method by analyzing attention modules and visualizing speaker embeddings.