Although few-shot learning has attracted much attention from the fields of image and audio classification, few efforts have been made on few-shot speaker identification. In the task of few-shot learning, overfitting is a tough problem mainly due to the mismatch between training and testing conditions. In this paper, we propose a few-shot speaker identification method which can alleviate the overfitting problem. In the proposed method, the model of a depthwise separable convolutional network with channel attention is trained with a prototypical loss function. Experimental datasets are extracted from three public speech corpora: Aishell-2, VoxCeleb1 and TORGO. Experimental results show that the proposed method exceeds state-of-the-art methods for few-shot speaker identification in terms of accuracy and F-score.
@article{arxiv.2204.11180,
title = {Few-Shot Speaker Identification Using Depthwise Separable Convolutional Network with Channel Attention},
author = {Yanxiong Li and Wucheng Wang and Hao Chen and Wenchang Cao and Wei Li and Qianhua He},
journal= {arXiv preprint arXiv:2204.11180},
year = {2022}
}
Comments
Accepted by Odyssey 2022 (The Speaker and Language Recognition Workshop 2022, Beijing, China)