
# Dataset Card for asvspoof2019

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-instances)
  - [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation 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}
}
```

task_categories:audio-classification annotations_creators:other language_creators:other multilinguality:monolingual size_categories:100K<n<1M source_datasets:extended|vctk language:en license:odc-by voice-anti-spoofing arxiv:1911.01601 region:us
{"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"]}
TAGS
#task_categories-audio-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|vctk #language-English #license-odc-by #voice-anti-spoofing #arxiv-1911.01601 #region-us 

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: URL
* Repository:
* Paper: URL
* Leaderboard:
* Point of Contact:


### Dataset Summary


This is a database used for the Third Automatic Speaker Verification Spoofing
and Countermeasuers Challenge, for short, ASVspoof 2019 (URL)
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


### Languages


English


Dataset Structure
-----------------


### Data Instances


### 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
* 'attack\_id': a duple (D\_a,Q), which take one letter in the set {A,B,C} as categorical value, defined as



for bonafide speech, 'attack\_id' is left blank ('-')
* 'key': 'bonafide' for genuine speech, or, 'spoof' for spoofing speech


### Data Splits



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### 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


This ASVspoof 2019 dataset is made available under the Open Data Commons Attribution License: URL



### Dataset Summary


This is a database used for the Third Automatic Speaker Verification Spoofing
and Countermeasuers Challenge, for short, ASVspoof 2019 (URL)
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 ### Languages


English


Dataset Structure
----------------- ### Data Instances ### 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
* 'attack\_id': a duple (D\_a,Q), which take one letter in the set {A,B,C} as categorical value, defined as



for bonafide speech, 'attack\_id' is left blank ('-')
* 'key': 'bonafide' for genuine speech, or, 'spoof' for spoofing speech ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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


This ASVspoof 2019 dataset is made available under the Open Data Commons Attribution License: URL
TAGS
#task_categories-audio-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|vctk #language-English #license-odc-by #voice-anti-spoofing #arxiv-1911.01601 #region-us 
 ### Dataset Summary


This is a database used for the Third Automatic Speaker Verification Spoofing
and Countermeasuers Challenge, for short, ASVspoof 2019 (URL)
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 ### Languages


English


Dataset Structure
----------------- ### Data Instances ### 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
* 'attack\_id': a duple (D\_a,Q), which take one letter in the set {A,B,C} as categorical value, defined as



for bonafide speech, 'attack\_id' is left blank ('-')
* 'key': 'bonafide' for genuine speech, or, 'spoof' for spoofing speech ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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


This ASVspoof 2019 dataset is made available under the Open Data Commons Attribution License: URL
passage: TAGS
#task_categories-audio-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|vctk #language-English #license-odc-by #voice-anti-spoofing #arxiv-1911.01601 #region-us 
### Dataset Summary


This is a database used for the Third Automatic Speaker Verification Spoofing
and Countermeasuers Challenge, for short, ASVspoof 2019 (URL)
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### Languages


English


Dataset Structure
-----------------### Data Instances