Latin-American voice anti-spoofing dataset

This dataset contains samples of spoof and real human voice with different accents from Latin-American countries.

                                  Table 1. Real samples distribution

Accent
Gender
# Speakers
# Files
Nomenclature

Colombian

Male

Female

17

14

2534

2070

com

cof

Chilean

Male

Female

17

12

2487

1602

clm

clf

Peruvian

Male

Female

20

18

2917

2529

pem

pef

Venezuelan

Male

Female

12

10

1754

1463

vem

vef

Argentinian

Male

Female

12

30

1670

3790

arm

arf

Total

162
22816

The bonafide samples were obtained from the following sources:

Colombian accents: https://www.openslr.org/72/ (License)

Chilean accents: https://www.openslr.org/71/ (License)

Peruvian accents: https://www.openslr.org/73/ (License)

Venezuelan accents: https://www.openslr.org/75/ (License)

Argentinian accents:  https://www.openslr.org/61/ (License)

The strategies used to generate the spoof samples:

                                        Table 2. Spoof Samples distribution

Name
Type
#Samples

StarGAN
Voice conversion
16000

CycleGAN
Voice conversion
16000

Diffusion
Voice conversion
16000

TTS
Text-to-speech
5000

TTS-StarGAN
Text-to-speech / Voice conversion
2500

TTS-Diff
Text-to-speech / Voice conversion
2500

StarGAN-VC: Non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks

Cyclegan-VC: Non-parallel voice conversion using cycle-consistent adversarial networks

Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme

TTS: Microsoft azure TTS

TTS-VC: Microsoft azure TTS + StarGAN/Diff

                                          Table 3. Dataset overview

Audio Samples
Human Speakers
Spoofing algorithms
Sampling rate

Bonafide
Spoof

22816
58000

Male
Female

78
84

VC
TTS
VC and TTS

3
1
2

16kHz

On the protocol.txt file is listed all the files with the following structure:

                                                               Subject_id  file_name –  spoof_type  Label

Consider this line on protocol.txt file:
arf_00295 StarGAN-arf _00295_01349969200-cof _03349 _0077577 - StarGAN spoof
The first part (arf_00295) represent the subject id, from which we can also identify the accent and the gender (see nomenclature column on Table 1). The file name identify the type of spoof following for the source audio file and the target file. StarGAN represents the type of spoof. According to the table 2, this method is a Voice Conversion algorithm. If the file is a bonafide sample, we replace the spoof_type with a dash (-). Finally at the end of the line we refer the kind of label of the file, in the example, the file corresponds to a spoof case.

Each zip file contains 6 folders, each one holds a type of samples. For the voice conversion folders, there are 25 sub-folders that indicate the conversion between accents. For example, Argentina-Venezuela folder indicates that the source accent of the file is Argentinian and the target is Venezuelan accent. Inside the folder there are 64 sub-folders that represent the subjects used for the conversion. For instance, the folder arf_00295-vem_04310 means that the source is an Argentinean female and the target is a Venezuelan male (see Table 1 for nomenclature). In the case of a Text-to-Speech folder there are 5 sub-folders that represent the accents. A TTS-VC folder there are 2 sub-folders that represent the voice conversion strategy used. Inside there are other sub-folders for the different combinations of source and target accents.

You can check the folder tree structure in the tree.txt file. Table 3 shows a summary of the resulting dataset.