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End-to-End attention based text-dependent speaker verification
2016
2016 IEEE Spoken Language Technology Workshop (SLT)
A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetic/speaker discriminative DNNs as feature extractors for speaker verification has shown promising results. The extracted frame-level (DNN bottleneck, posterior or d-vector) features are equally weighted and aggregated to compute an utterance-level speaker representation (d-vector or i-vector). In this work we use speaker discriminative CNNs to extract the noise-robust
doi:10.1109/slt.2016.7846261
dblp:conf/slt/ZhangCZLG16
fatcat:ar7kuwixsbalbigtu5aghppll4