Invention Grant
- Patent Title: End-to-end speaker recognition using deep neural network
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Application No.: US16536293Application Date: 2019-08-08
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Publication No.: US11468901B2Publication Date: 2022-10-11
- Inventor: Elie Khoury , Matthew Garland
- Applicant: PINDROP SECURITY, INC.
- Applicant Address: US GA Atlanta
- Assignee: PINDROP SECURITY, INC.
- Current Assignee: PINDROP SECURITY, INC.
- Current Assignee Address: US GA Atlanta
- Agency: Foley & Lardner LLP
- Main IPC: G10L17/08
- IPC: G10L17/08 ; G10L15/16 ; G10L17/02 ; G10L17/04 ; G10L17/22 ; G06N3/04 ; G06N3/08 ; G10L17/18

Abstract:
The present invention is directed to a deep neural network (DNN) having a triplet network architecture, which is suitable to perform speaker recognition. In particular, the DNN includes three feed-forward neural networks, which are trained according to a batch process utilizing a cohort set of negative training samples. After each batch of training samples is processed, the DNN may be trained according to a loss function, e.g., utilizing a cosine measure of similarity between respective samples, along with positive and negative margins, to provide a robust representation of voiceprints.
Public/Granted literature
- US20190392842A1 END-TO-END SPEAKER RECOGNITION USING DEEP NEURAL NETWORK Public/Granted day:2019-12-26
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