CN-Celeb2022是一项针对真实世界复杂场景和实时处理速度的语音识别挑战，包括娱乐、访谈、唱歌等多种类型的数据。比赛分为固定轨道和开放轨道两个任务，分别涉及说话人验证和说话人检索。评价标准包括最小检测成本函数、等错误率和决策错误贸易曲线。参赛者可以使用CN-Celeb训练集或其他任何数据源进行系统开发，并通过提交系统评估结果参与竞赛。
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.
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 sour
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