Speaker embeddings for Text-independent speaker verification using TensorFlow, with Kaldi

This is a slightly modified TensorFlow implementation of the model presented by David Snyder in Deep Neural Network Embeddings for Text-Independent Speaker Verification.

In the paper, this algorithm is a little worse than i-vector. My test show similar output. Also, in my test, shallow network was a very little worse than deep network (This is dependency of DB).

In this code, there are many hard cording such folder location and some parameter related database. If I have database well-known SR database, I try to it. but I only have private database.

I hope this code helps researcher.

Credits

Original paper:

• Snyder's paper:

@unknown{unknown,
author = {Snyder, David and Garcia-Romero, Daniel and Povey, Daniel and Khudanpur, Sanjeev},
title = {Deep Neural Network Embeddings for Text-Independent Speaker Verification},
year = {2017}
}

Also, use the part of code:

• mangate's git repository

• tensorflow classification baseline code

• Karel Vesely's git repository

• kaldi io for python

Features

• Supports kaldi input&output style(input : mfcc scp-ark pair, output : embedding scp-ark pair)

• This code can replace i-vector train - extraction part in kaldi egs/SRE10/v1.

• Instead of concatenate VAD frame, I use orginal frame contain non-speech frame.

• Training case, Many frame was used to train. Test case, max power frame to test. Detail is in the process_data_kaldi.py load_dataset function

• This part depend on your opinion.

• Adding input layer mean normalization instead of exptional block.

• Adding some layer dropout and Batch normalized.

• Adding L2 loss in last layer.

Requirements

• Python (2.7)

• NumPy

• TensorFlow (I tried only 1.3 version)

• Database

Usage

Preperation:

1. Clone the repository recursively to get all folder and subfolders

2. Prepare Database(I use private DB. If you need, the script needs to be modified)

3. Use Kaldi-recipe extracing MFCC and VAD in SRE10/v1/run.sh

Running:

1. run Training_kaldi function in make_dvec.py.
   after, run embedding_kaldi function.(Some function was written hard cording. Change you file location)

2. use kaldi-recipe calculating mean vector and PLDA scoring.
   Maybe, you only run after /local/extract_ivectors.sh --stage 2 each folder.

Authors

qqueing@gmail.com( or kindsinu@naver.com)