Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System

Weicheng Cai; Jinkun Chen; Ming Li

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arXiv · 1804.05160

Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System

Weicheng Cai·Jinkun Chen·Ming Li

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Abstract

In this paper, we explore the encoding/pooling layer and loss function in the end-to-end speaker and language recognition system. First, a unified and interpretable end-to-end system for both speaker and language recognition is developed. It accepts variable-length input and produces an utterance level result. In the end-to-end system, the encoding layer plays a role in aggregating the variable-length input sequence into an utterance level representation. Besides the basic temporal average pooling, we introduce a self-attentive pooling layer and a learnable dictionary encoding layer to get the utterance level representation. In terms of loss function for open-set speaker verification, to get more discriminative speaker embedding, center loss and angular softmax loss is introduced in the end-to-end system. Experimental results on Voxceleb and NIST LRE 07 datasets show that the performance of end-to-end learning system could be significantly improved by the proposed encoding layer and loss function.

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BibTeX RIS

Weicheng Cai, Jinkun Chen, Ming Li. 2018-04-14. Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System. https://arxiv.org/abs/1804.05160

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