INTERSPEECH2022丨希尔贝壳邀您参加 FFSVC 2022 远场说话人识别比赛
Welcome to FFSVC 2022! The success of FFSVC2020 indicates that more and more researchers are paying attention to the far-field speaker verification task. In this year, the challenge still focuses on the far-field speaker verification task and provides a new far-field development and test set collected by real speakers in complex environments under multiple conditions, e.g., text-dependent, text-independent, cross-channel enroll/test, etc. In addition, in-domain training speech data may be unlabeled, which is difficult to fine-tune the pre-trained model. Therefore, a new focus of this year is cross-language self-supervised / semi-supervised learning, where participants are allowed to use the unlabeled train and dev set of the in-domain FFSVC2020 dataset (in Mandarin) and the labeled out-of-domain VoxCeleb 1&2 dataset (mostly in English) to build the model.
Releasing the FFSVC 2022 evaluation plan and starting the registration
Task Description
This year we focus on the far-fieldsingle-channelscenarios. There are two tasks in this challenge; both tasks are to determine whether two speech samples are from the same speaker:
Task 1. Fully supervised far-field speaker verification.
Task 2. Semi-supervised far-field speaker verification.
We define the task 1&2 as fixed training conditions that the participants can only use a fixed training set to build the speaker verification system. The fixed training set consists of the following two databases:
VoxCeleb 1&2.
FFSVC2020 dataset (Train and dev set).
FFSVC2020 supplementary set(new!).
Note:Please refer to thiswebsiteto download VoxCeleb 1&2 dataset and thiswebsiteto download FFSVC 2020 dataset if you do not have these two datasets. In addition, in this challenge, we release a supplementary set of FFSVC2020, which consists of the same devices data as FFSVC2022.
In task 1, participants can use both VoxCeleb1&2 and FFSVC20 datasets with speaker labels to train a far-field speaker verification system.
For task 2, in contrast to task1participants cannot use the speaker labels of the FFSVC2020 dataset. In task 2, we encourage the participants to adopt self-supervised or semi-supervised methods to utilize the in-domain unlabeled data.
Using other speech datasets to train the system is forbidden, while participants are allowed to use public open-source non-speech dataset to perform data augmentation. The self-supervised pre-trained models, such as Wav2Vec and WavLM, cannot be used in this challenge.
Schedule
April 15th, 2022 : Releasing the FFSVC 2022 evaluation plan and starting the registration.
April 20th, 2022 : Opening the submission system and releasing supplementary/dev/eval sets
July 3th, 2022:Deadline for registration.
July 10th, 2022 : Deadline for results submission.
July 15th, 2022 : Deadline for system description submission
July 24th, 2022 : Deadline for workshop paper submission
Aug 20th, 2022 : Workshop paper acceptance notification
Sep 17th, 2022 : Interspeech 2022 Satellite Workshop
Prizes
Prizes will be awarded to top three winning teams of each task.
Papers
Evaluation Plan
Far-field Speaker Verification Challenge (FFSVC) 2022 : Challenge Evaluation Plan
Qin, Xiaoyi, Li, Ming, Bu, Hui, Narayanan, Shrikanth, and Li, Haizhou
2022
This document is the description of Far-field Speaker Verification Challenge (FFSVC) 2022.
Papers
Far-field Speaker Verification Challenge (FFSVC) 2022 : Challenge Evaluation Plan
Qin, Xiaoyi, Li, Ming, Bu, Hui, Narayanan, Shrikanth, and Li, Haizhou
2022
BIB
@article{FFSVC2022_Eval_Plan
abbr = {Evaluation Plan}
bibtex_show = {true}
title = {Far-field Speaker Verification Challenge (FFSVC) 2022 : Challenge Evaluation Plan}
author = {Qin, Xiaoyi and Li, Ming and Bu, Hui and Narayanan, Shrikanth and Li, Haizhou}
journal = {}
html = {https://ffsvc.github.io/assets/pdf/ffsvc2022_plan_v2.pdf}
pdf = {ffsvc2022_plan_v2.pdf}
selected = {true}
year = {2022}
}
Register
Since the challenge will be held on the Codalab platform, please create a Codalab account if you do not have one. We kindly request you to associate your account to an institutional e-mail. The organizing committee reserves the right to revoke your participant to the challenge otherwise, please readevaluation plancarefully. Make sure to set the name of your team in the user's profile, or it will not be visible on the leaderboard.
The following is the Codalab links corresponding to each task:
- Task 1. Fully supervised far-field speaker verification.
- Task 2. Semi-supervised far-field speaker verification.
Participants can register in one or two tasks. If your team participates in multiple tasks, we kindly request you to use the same user account to participate in all tasks.
Please note that any deliberate attempts to bypass the submission limit (for instance, by creating multiple accounts and using them to submit) will lead to automatic disqualification.
Submission
Participants are required to submit at least one valid score file for each participating task to the Codalab platform. Clicking on the “Submit/View Results” link under the “Participants” tab could submit your score file. You must submit your results in the form of a ZIP file, containing only one file named “scores.txt”. The file must be at the root of the ZIP file and the ZIP file should not contain any folders. The score files should be in UTF-8 format with one line per trial.
- Download score example file.
Organisers
Ming Li, Duke Kunshan University, China
Haizhou Li, National University of Singapore, Singapore
Shrikanth Narayanan, University of Southern California, USA
Hui Bu, AI Shell Foundation,China
Xiaoyi Qin, Duke Kunshan University, China
Yao Shi, Duke Kunshan University, China
FFSVC 2020 Website
FFSVC 2022