README.md

metadata

annotations_creators:
  - other
language_creators:
  - other
language:
  - en
license:
  - odc-by
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets:
  - extended|vctk
task_categories:
  - audio-classification
task_ids: []
pretty_name: asvspoof2019
tags:
  - voice-anti-spoofing

Dataset Card for asvspoof2019

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information

Dataset Description

• Homepage: https://datashare.ed.ac.uk/handle/10283/3336
• Repository: [Needs More Information]
• Paper: https://arxiv.org/abs/1911.01601
• Leaderboard: [Needs More Information]
• Point of Contact: [Needs More Information]

Dataset Summary

This is a database used for the Third Automatic Speaker Verification Spoofing and Countermeasuers Challenge, for short, ASVspoof 2019 (http://www.asvspoof.org) organized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, Héctor Delgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman, and Andreas Nautsch in 2019.

Supported Tasks and Leaderboards

[Needs More Information]

Languages

English

Dataset Structure

Data Instances

{'speaker_id': 'LA_0091',
 'audio_file_name': 'LA_T_8529430',
 'audio': {'path': 'D:/Users/80304531/.cache/huggingface/datasets/downloads/extracted/8cabb6d5c283b0ed94b2219a8d459fea8e972ce098ef14d8e5a97b181f850502/LA/ASVspoof2019_LA_train/flac/LA_T_8529430.flac',
  'array': array([-0.00201416, -0.00234985, -0.0022583 , ...,  0.01309204,
          0.01339722,  0.01461792], dtype=float32),
  'sampling_rate': 16000},
 'system_id': 'A01',
 'key': 1}

Data Fields

Logical access (LA):

• speaker_id: LA_****, a 4-digit speaker ID
• audio_file_name: name of the audio file
• audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should always be preferred over dataset["audio"][0].
• system_id: ID of the speech spoofing system (A01 - A19), or, for bonafide speech SYSTEM-ID is left blank ('-')
• key: 'bonafide' for genuine speech, or, 'spoof' for spoofing speech

Physical access (PA):

• speaker_id: PA_****, a 4-digit speaker ID

• audio_file_name: name of the audio file

• audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should always be preferred over dataset["audio"][0].

• environment_id: a triplet (S,R,D_s), which take one letter in the set {a,b,c} as categorical value, defined as

  	a	b	c
  S: Room size (square meters)	2-5	5-10	10-20
  R: T60 (ms)	50-200	200-600	600-1000
  D_s: Talker-to-ASV distance (cm)	10-50	50-100	100-150

• attack_id: a duple (D_a,Q), which take one letter in the set {A,B,C} as categorical value, defined as

  	A	B	C
  Z: Attacker-to-talker distance (cm)	10-50	50-100	> 100
  Q: Replay device quality	perfect	high	low

  for bonafide speech, attack_id is left blank ('-')

• key: 'bonafide' for genuine speech, or, 'spoof' for spoofing speech

Data Splits

	Training set	Development set	Evaluation set
Bonafide	2580	2548	7355
Spoof	22800	22296	63882
Total	25380	24844	71237

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

This ASVspoof 2019 dataset is made available under the Open Data Commons Attribution License: http://opendatacommons.org/licenses/by/1.0/

Citation Information

@InProceedings{Todisco2019,
  Title     = {{ASV}spoof 2019: {F}uture {H}orizons in {S}poofed and {F}ake {A}udio {D}etection},
  Author    = {Todisco, Massimiliano and
               Wang, Xin and
               Sahidullah, Md and
               Delgado, H ́ector and
               Nautsch, Andreas and
               Yamagishi, Junichi and
               Evans, Nicholas and
               Kinnunen, Tomi and
               Lee, Kong Aik},
  booktitle = {Proc. of Interspeech 2019},
  Year      = {2019}
}