Keras implimentation of "Deep Learning & 3D Convolutional Neural Networks for Speaker Verification"
This code is aimed to provide the implementation for Speaker Verification (SR) by using 3D convolutional neural networks following the SR protocol.
The input pipeline is implimented in ./dataset.py for a more detailed description...
The MFCC features can be used as the data representation of the spoken utterances at the frame level. However, a drawback is their non-local characteristics due to the last DCT 1 operation for generating MFCCs. This operation disturbs the locality property and is in contrast with the local characteristics of the convolutional operations. The employed approach in this work is to use the log-energies, which we call MFECs. The extraction of MFECs is similar to MFCCs by discarding the DCT operation. The temporal features are overlapping 20ms windows with the stride of 10ms, which are used for the generation of spectrum features. From a 0.8- second sound sample, 80 temporal feature sets (each forms a 40 MFEC features) can be obtained which form the input speech feature map. Each input feature map has the dimen- sionality of ζ × 80 × 40 which is formed from 80 input frames and their corresponding spectral features, where ζ is the number of utterances used in modeling the speaker during the development and enrollment stages.
If you used this code please kindly cite 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} }
The speech features have been extracted using [SpeechPy]_ package.
See also the list of contributors who participated in this project.
This project is licensed under the MIT License - see the LICENSE.md file for details
