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:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification
| Date | Stars |
|---|---|
| 2026-07-31 | 790 |
| 2026-08-03 | 790 |
| 2026-08-06 | 790 |
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TensorFlow implementation of 3D Convolutional Neural Networks for Speaker Verification - `Official Project Page`_ - `Pytorch Implementation`_
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.. image:: https://codecov.io/gh/astorfi/3D-convolutional-speaker-recognition/branch/master/graph/badge.svg
:target: https://codecov.io/gh/astorfi/3D-convolutional-speaker-recognition
.. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat
:target: https://github.com/astorfi/3D-convolutional-speaker-recognition/pulls
.. image:: https://badges.frapsoft.com/os/v2/open-source.svg?v=102
:target: https://github.com/ellerbrock/open-source-badge/
.. image:: https://zenodo.org/badge/94718341.svg
:target: https://zenodo.org/badge/latestdoi/94718341
.. image:: https://img.shields.io/twitter/follow/amirsinatorfi.svg?label=Follow&style=social
:target: https://twitter.com/amirsinatorfi
This repository contains the code release for our paper titled as *"Text-Independent
Speaker Verification Using 3D Convolutional Neural Networks"*. The link to the paper_ is
provided as well.
.. _Official Project Page: https://codeocean.com/2017/08/01/3d-convolutional-neural-networks-for-speaker-recognition/code
.. _Pytorch Implementation: https://github.com/astorfi/3D-convolutional-speaker-recognition-pytorch
.. _paper: https://arxiv.org/abs/1705.09422
.. _TensorFlow: https://www.tensorflow.org/
The code has been developed using TensorFlow_. The input pipeline must be prepared by the users.
This code is aimed to provide the implementation for Speaker Verification (SR) by using 3D convolutional neural networks
following the SR protocol.
.. image:: readme_images/conv_gif.gif
:target: https://github.com/astorfi/3D-convolutional-speaker-recognition/blob/master/_images/conv_gif.gif
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Citation
------------
If you used this code, please kindly consider citing the following paper:
.. code:: shell
@article{torfi2017text,
title={Text-independent speaker verification using 3d convolutional neural networks},
author={Torfi, Amirsina and Nasrabadi, Nasser M and Dawson, Jeremy},
journal={arXiv preprint arXiv:1705.09422},
year={2017}
}
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DEMO
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For running a demo, after forking the repository, run the following scrit:
.. code:: shell
./run.sh
|speakerrecognition|
.. |speakerrecognition| image:: readme_images/speakerrecognition.png
:target: https://asciinema.org/a/yfy6FryUAWWMl1vgylrRagMdw
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General View
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We leveraged 3D convolutional architecture for creating the speaker model in order to simultaneously
capturing the speech-related and temporal information from the speakers' utterances.
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Speaker Verification Protocol(SVP)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In this work, a 3D Convolutional Neural Network (3D-CNN)
architecture has been utilized for text-independent speaker
verification in three phases.
1. At the **development phase**, a CNN is trained
to classify speakers at the utterance-level.
2. In the **enrollment stage**, the trained network is utilized to directly create a
speaker model for each speaker based on the extracted features.
3. Finally, in the **evaluation phase**, the extracted features
from the test utterance will be compared to the stored speaker
model to verify the claimed identity.
The aforementioned three phases are usually considered as the SV protocol. One of the main
challenges is the creation of the speaker models. Previously-reported approaches create
speaker models based on averaging the extracted features from utterances of the speaker,
which is known as the d-vector system.
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HoExcerpt of 11,484 characters
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Sina Torfi · Meta · United States
89
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:77282936802a4d46, topic:deep-learning