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arXiv · 1505.06427

Deep Speaker Vectors for Semi Text-independent Speaker Verification

Lantian Li·Dong Wang·Zhiyong Zhang·Thomas Fang Zheng
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Abstract

Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification. This new method has been tested on text-dependent speaker verification tasks, and improvement was reported when combined with the conventional i-vector method. This paper extends the d-vector approach to semi text-independent speaker verification tasks, i.e., the text of the speech is in a limited set of short phrases. We explore various settings of the DNN structure used for d-vector extraction, and present a phone-dependent training which employs the posterior features obtained from an ASR system. The experimental results show that it is possible to apply d-vectors on semi text-independent speaker recognition, and the phone-dependent training improves system performance.

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Lantian Li, Dong Wang, Zhiyong Zhang, Thomas Fang Zheng. 2015-05-24. Deep Speaker Vectors for Semi Text-independent Speaker Verification. https://arxiv.org/abs/1505.06427

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