RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification

• Currently accepted to Interspeech2019 as a conference paper.
• arXiv pre-print is at https://arxiv.org/abs/1904.08104

1. Script 'lunch_ngc.sh' is used to create a virtual environment for DNN training using NGC(nvidia gpu cloud).
2. Script '00-pre_process_waveforms.py' was conducted in another workstation when we reproduced experiemnts regarding RawNet.
3. For back-end research or front-end verification, we provide speaker embeddings extracted with RawNet at 'data/speaker_embeddings_RawNet'. 
	Cosine similarity metric with this embeddings demonstrate EER of 4.8 % on the VoxCeleb1 evaluation set. 
	This file can also obtained by running script '01-trn_RawNet.py' (minor differences can occur due to random seed).

'data/speaker_embeddings_RawNet_4.8eer' contains speaker embeddings extracted using RawNet. Load it using python pickle library, a dictionary will be obtained. It has two keys: ['dev_dic_embeddings', 'eval_dic_embeddings'] where each value corresponding to the key is a dictionary that has speaker embeddings. Decoding with cosine similarity with VoxCeleb1 dataset will yield an EER of 4.8 %. In our paper, training a b-vector classifier using these embeddings yielded an EER of 4.0 %.

For other back-end researches on speaker verification, using these speaker embeddings might be a good start :)

Email [email protected] for other details :-).

Log

• 2019.04.17. : 01 script executing
• 2019.04.24. : 01 script verified.
• 2019.04.29. : 02 script executing
• 2019.04.29. : 02 script verified.