Datasets:
Tasks:
Audio Classification
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
voice-anti-spoofing
License:
| 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-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} | |
| } | |
| ``` | |