End-to-end speaker recognition using deep neural network
AssigneePindrop Security, Inc.
InventorsElie Khoury, Matthew Garland
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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.
Claims26
A speaker recognition device comprising:
- a memory device storing speech samples including,
- dual sets of speech samples attributed to the same speaker,
- a cohort set of speech samples not attributed to the same speaker as the dual sets, and
- a set of speaker models;
- and
- a processor-based device configured to model a deep neural network with a triplet network architecture,
- wherein the processor-based device trains the deep neural network according to a batch process in which the dual sets of speech samples are fed through the deep neural network in combination with the cohort set of speech samples, and
- wherein the processor-based device feeds a recognition speech sample through the trained deep neural network, and verifies or identifies a user based on an output of the trained deep neural network responsive to the recognition speech sample and at least one of the speaker models.
A method comprising:
- training a computer-implemented model of a deep neural network with a triplet network architecture based on a plurality of speech samples stored in a memory device, the plurality of speech samples including:
- dual sets of speech samples attributed to the same speaker,
- a cohort set of speech samples not attributed to the same speaker as the dual sets, and
- a set of speaker models;
- feeding a recognition speech sample through the trained deep neural network, and verifying or identifying a user based an output of the trained deep neural network responsive to the recognition speech sample and at least one of the speaker models,
- wherein the training of the deep neural network is performed according to a batch process in which the dual sets of speech samples are fed through the deep neural network in combination with the cohort set of speech samples.
Dependencies are inferred from the claim text.