Odyssey 2022丨CNSRC 2022 说话人识别竞赛

Welcome to the first CN-Celeb speaker recognition challenge, CNSRC 2022 ! The challenge aims to probe how well the current speaker recognition methods can work in real world scenarios, including with in-the-wild complexity and real-time processing speed.

The challenge will be based on CN-Celeb, a free multi-genre speaker recognition dataset with the most real-world complexity so far. The dataset consists of audio from both multiple genres of speech, including entertainment, interview, singing, play, movie, vlog, live broadcast, speech, drama, recitation and advertisement as well as real-world noise, strong and overlapped background speakers, significant variations in speaking styles, time-varying and cross-channel problems and long-short test scenarios. The CNSRC 2022 is open now. Please check the detailed information below about the challenge.

CNSRC 2022 Evaluation Plan

Tasks

CNSRC 2022 defines two tasks: speaker verification (SV) and speaker retrieval (SR).

Task 1. Speaker Verification (SV)

The objective of this task is to improve performance on the standard CN-Celeb evaluation set. According to the data used in system development, two tracks are defined for the SV task: fixed track and open track, shown as follows:

Fixed Track, where only the CN-Celeb training set is allowed for training/tuning the system.

Open Track, where any data sources can be used for developing the system, except the CN-Celeb evaluation set.

Task 2. Speaker Retrieval (SR)

The purpose of this task is to find out the utterances spoken by a target speaker from a large data pool, given an enrollment data of the target speaker. Each target speaker forms a retrieval request. Each target individual has 1 enrolled utterance and 10 test utterances. The non-target set contains a large amount of utterances, coming from multiple sources. The target and non-target utterances are put together, and the participants are required to design their retrieval system to find top-10 candidates for each target speaker, and list them in descending order according to the LLR scores. Participants can use any data sources to train their system, except the CN-Celeb evaluation set.

CNSRC 2022 defines two tasks: speaker verification (SV) and speaker retrieval (SR).

Evaluation

Task 1. Speaker Verification (SV)

The primary metric for SV performance evaluation is minimum Detection Cost Function (minDCF).

Firstly define the detection cost function as follows:

where