View source for 18485069. Neural Networks For Speaker Verification simplified abstract (GOOGLE LLC)

=Neural Networks For Speaker Verification=


==Organization Name==

[[:Category:GOOGLE LLC|GOOGLE LLC]]


[[Category:GOOGLE LLC]]

==Inventor(s)==

[[:Category:Georg Heigold of Mountain View CA (US)|Georg Heigold of Mountain View CA (US)]][[Category:Georg Heigold of Mountain View CA (US)]]

[[:Category:Samuel Bengio of Los Altos CA (US)|Samuel Bengio of Los Altos CA (US)]][[Category:Samuel Bengio of Los Altos CA (US)]]

[[:Category:Ignacio Lopez Moreno of New York NY (US)|Ignacio Lopez Moreno of New York NY (US)]][[Category:Ignacio Lopez Moreno of New York NY (US)]]

==Neural Networks For Speaker Verification - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18485069 titled 'Neural Networks For Speaker Verification

==Simplified Explanation==

The patent application describes systems and methods for speaker verification using neural networks. Here are the key points:

* The method involves training a neural network for a speaker verification model.
* Users are enrolled at a client device, and their identities are verified based on characteristics of their voices.
* The method includes receiving data that characterizes an utterance of a user.
* A speaker representation is generated for the utterance using a neural network on a computing device.
* The neural network is trained based on a plurality of training samples that include data characterizing utterances labeled as matching or non-matching speakers.

Potential applications of this technology:

* Speaker verification for secure access control systems.
* Voice-based authentication for mobile devices and online platforms.
* Fraud detection in call centers and financial transactions.
* Personalized voice assistants that can identify individual users.

Problems solved by this technology:

* Enhances security by verifying the identity of users based on their voices.
* Reduces the risk of unauthorized access to sensitive information.
* Provides a convenient and efficient method for user authentication.

Benefits of this technology:

* Improved accuracy in speaker verification compared to traditional methods.
* Scalable and adaptable to different languages and dialects.
* Can be integrated into existing systems and devices.
* Reduces the need for passwords or PINs, enhancing user experience.


==Original Abstract Submitted==

This document generally describes systems, methods, devices, and other techniques related to speaker verification, including (i) training a neural network for a speaker verification model, (ii) enrolling users at a client device, and (iii) verifying identities of users based on characteristics of the users' voices. Some implementations include a computer-implemented method. The method can include receiving, at a computing device, data that characterizes an utterance of a user of the computing device. A speaker representation can be generated, at the computing device, for the utterance using a neural network on the computing device. The neural network can be trained based on a plurality of training samples that each: (i) include data that characterizes a first utterance and data that characterizes one or more second utterances, and (ii) are labeled as a matching speakers sample or a non-matching speakers sample.



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