End-to-end DNN Speaker recognition system

Reseach leader:Plchot Oldřich
Team leaders:Matějka Pavel
Team members:Novotný Ondřej, Silnova Anna
Agency:Google
Start:2017-03-07
End:2018-09-06
Keywords:speaker recognition, DDN system
Annotation:
Text-independent speaker verification (SV) is currently the only bastion in the domain of speech data mining that resists the massive attack of deep neural networks (DNNs). We have already seen the end-to-end DNN approach to yield very good performance in the area of text-dependent SV and DNNs have been very successful in the related domain of spoken language recognition. In text-independent SV, features and frame-alignment based on DNNs has helped too, but so far, to end-to-end DNN system has matched the performance of iVectors. In this project, we will depart from existing DNN approaches for SV and advance towards fullDNN systems, mainly with the help of recently introduced Sequence-summarizing Neural Networks (SSNN). We will work on big and diverse datasets by utilizing large amount of publicly available data in combination with exploring efficient input feature representations.

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