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Deep Speaker Embedding with Long Short Term Centroid Learning for Text-Independent Speaker Verification
2020
Interspeech 2020
Recently, speaker verification systems using deep neural networks have shown their effectiveness on large scale datasets. The widely used pairwise loss functions only consider the discrimination within a mini-batch data (short-term), while either the speaker identity information or the whole training dataset is not fully exploited. Thus, these pairwise comparisons may suffer from the interferences and variances brought by speakerunrelated factors. To tackle this problem, we introduce the
doi:10.21437/interspeech.2020-2470
dblp:conf/interspeech/PengGZ20
fatcat:s6sq6ix3zjbe7hhr2xsfcjt5fy