AI News, Using 3D Convolutional Neural Networks for Speaker Verification

Using 3D Convolutional Neural Networks for Speaker Verification

This repository contains the code release for our paper titled as 'Text-Independent Speaker

code is aimed to provide the implementation for Speaker Verification (SR) by using 3D convolutional neural networks following

If you used this code, please kindly consider citing the following paper: For running a demo, after forking the repository, run the following scrit:

We leveraged 3D convolutional architecture for creating the speaker model in order to simultaneously capturing

In the enrollment stage, the trained network is utilized to directly create a speaker

models based on averaging the extracted features from utterances of the speaker, which

In our paper, we propose the implementation of 3D-CNNs for direct speaker model creation in

The MFCC features can be used as the data representation of the spoken utterances at the frame level.

This operation disturbs the locality property and is in contrast with the local characteristics of the convolutional operations.

sound sample, 80 temporal feature sets (each forms a

of ζ × 80 × 40 which is formed from 80 input frames

The code architecture part has been heavily inspired by Slim and Slim image classification library.

If you used this code please kindly cite the following paper: The license is as follows: Please refer to LICENSE file for further detail.

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