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arXiv · 2204.11180

Few-Shot Speaker Identification Using Depthwise Separable Convolutional Network with Channel Attention

Yanxiong Li·Wucheng Wang·Hao Chen·Wenchang Cao·Wei Li·Qianhua He
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Abstract

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.

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Yanxiong Li, Wucheng Wang, Hao Chen, Wenchang Cao, Wei Li, Qianhua He. 2022-04-24. Few-Shot Speaker Identification Using Depthwise Separable Convolutional Network with Channel Attention. https://arxiv.org/abs/2204.11180

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