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arXiv · 1705.03670

Deep Speaker Feature Learning for Text-independent Speaker Verification

Lantian Li·Yixiang Chen·Ying Shi·Zhiyuan Tang·Dong Wang
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Abstract

Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, either neural or probabilistic, has to be used to address the residual uncertainty when applied to speaker verification, just as with raw features. This paper presents a convolutional time-delay deep neural network structure (CT-DNN) for speaker feature learning. Our experimental results on the Fisher database demonstrated that this CT-DNN can produce high-quality speaker features: even with a single feature (0.3 seconds including the context), the EER can be as low as 7.68%. This effectively confirmed that the speaker trait is largely a deterministic short-time property rather than a long-time distributional pattern, and therefore can be extracted from just dozens of frames.

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Lantian Li, Yixiang Chen, Ying Shi, Zhiyuan Tang, Dong Wang. 2017-05-10. Deep Speaker Feature Learning for Text-independent Speaker Verification. https://arxiv.org/abs/1705.03670

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