Channel-compensated low-level features for speaker recognition
US10854205B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 8, 2019 |
| Grant date | Dec 1, 2020 |
| Priority date | — |
| Expiry date | Jul 8, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L19/028
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system for generating channel-compensated features of a speech signal includes a channel noise simulator that degrades the speech signal, a feed forward convolutional neural network (CNN) that generates channel-compensated features of the degraded speech signal, and a loss function that computes a difference between the channel-compensated features and handcrafted features for the same raw speech signal. Each loss result may be used to update connection weights of the CNN until a predetermined threshold loss is satisfied, and the CNN may be used as a front-end for a deep neural network (DNN) for speaker recognition/verification. The DNN may include convolutional layers, a bottleneck features layer, multiple fully-connected layers and an output layer. The bottleneck features may be used to update connection weights of the convolutional layers, and dropout may be applied to the convolutional layers.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.