TY  - GEN
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
TI  - Contrastive separative coding for self-supervised representation learning
EP  - 3869
SP  - 3865
AV  - public
Y1  - 2021///
KW  - Speaker verification
KW  -  speech separation
KW  -  self attention
KW  -  contrastive loss
A1  - Wang, J
A1  - Lam, MWY
A1  - Su, D
A1  - Yu, D
ID  - discovery10154106
N2  - To extract robust deep representations from long sequential modeling of speech data, we propose a self-supervised learning approach, namely Contrastive Separative Coding (CSC). Our key finding is to learn such representations by separating the target signal from contrastive interfering signals. First, a multi-task separative encoder is built to extract shared separable and discriminative embedding; secondly, we propose a powerful cross-attention mechanism performed over speaker representations across various interfering conditions, allowing the model to focus on and globally aggregate the most critical information to answer the "query"(current bottom-up embedding) while paying less attention to interfering, noisy, or irrelevant parts; lastly, we form a new probabilistic contrastive loss which estimates and maximizes the mutual information between the representations and the global speaker vector. While most prior unsupervised methods have focused on predicting the future, neighboring, or missing samples, we take a different perspective of predicting the interfered samples. Moreover, our contrastive separative loss is free from negative sampling. The experiment demonstrates that our approach can learn useful representations achieving a strong speaker verification performance in adverse conditions.
PB  - IEEE
UR  - https://doi.org/10.1109/ICASSP39728.2021.9414352
ER  -