speaker recognition using wavelet packet entropy, i-vector, and cosine distance scoring

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ID: 163485
2017
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Abstract
Today, more and more people have benefited from the speaker recognition. However, the accuracy of speaker recognition often drops off rapidly because of the low-quality speech and noise. This paper proposed a new speaker recognition model based on wavelet packet entropy (WPE), i-vector, and cosine distance scoring (CDS). In the proposed model, WPE transforms the speeches into short-term spectrum feature vectors (short vectors) and resists the noise. I-vector is generated from those short vectors and characterizes speech to improve the recognition accuracy. CDS fast compares with the difference between two i-vectors to give out the recognition result. The proposed model is evaluated by TIMIT speech database. The results of the experiments show that the proposed model can obtain good performance in clear and noisy environment and be insensitive to the low-quality speech, but the time cost of the model is high. To reduce the time cost, the parallel computation is used.
Reference Key
lei2017journalspeaker Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Lei Lei;She Kun
Journal Molecular diversity
Year 2017
DOI
10.1155/2017/1735698
URL
http://dx.doi.org/10.1155/2017/1735698
Keywords
computer engineering computer hardware

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