2022年7月4日，中国科学院声学研究所、西北工业大学、新加坡A*STAR信息通信研究所、上海交通大学以及Magic Data (北京爱数智慧科技有限公司) 联合主办的 “ISCSLP2022对话短语音说话人日志挑战赛” (ISCSLP 2022 Conversational Short-phrase Speaker Diarization Challenge, CSSD) 正式开启报名，欢迎学术界、产业界的团体及个人报名参赛。
围绕这一难题，我们开源了 MagicData-RAMC中文对话语音数据集，其中包含 180 小时人工标注对话语音数据。同时针对CSSD测评，我们还准备了 20 小时对话测试数据，并人工对说话人时间点进行了精准标注。针对CSSD挑战，我们同时设计了一个新的准确度评估指标，用于计算句子层面说话人分割聚类的精度。通过推动对话数据分割聚类技术的研究，我们旨在进一步促进该领域的可重复研究。
挑战赛相关问题，可以邮件标题为“对话短语音说话人日志挑战赛疑问”发送邮件至iscslp.email@example.com 或 firstname.lastname@example.org。
比赛分别设置一等奖、二等奖和三等奖，将评选出三组获奖团队/个人，获奖者将有机会参加 ISCSLP 2022 会议进行报告分享。
On July 4, 2022, ISCSLP 2022 Conversational Short-phrase Speaker Diarization Challenge (CSSD) which is jointly sponsored by the Institute of Acoustics CAS, Northwestern Polytechnical University, Singapore A*STAR Institute of Information and Communication, Shanghai Jiaotong University and Magic Data (Beijing Aishu Smart Technology Co., Ltd.), is officially opened for registration. Groups and individuals from academia and industry are welcome to register for the competition.
CSSD Challenge Registration : https://magichub.com/join-competition/?id=11559
Dialogue scenarios are one of the most essential and challenging scenarios for speech processing technology. In daily conversations, people casually respond to each other and continue the conversation with coherent questions and comments rather than bluntly answering each other's questions. Accurately detecting the speech activity of each person in a conversation is critical for many downstream tasks such as natural language processing and machine translation. The evaluation metric for speaker classification systems, the classification error rate (DER), has long been used as a standard evaluation metric for speaker classification. However, it fails to pay enough attention to short dialogue phrases. These short dialogue phrases are short but play an essential role at the semantic level. The speech community also lacks evaluation metrics to effectively assess the accuracy of short speech classification in conversations.
To solve this problem, we open-sourced the MagicData-RAMC Chinese conversational speech dataset, which contains 180 hours of manually annotated conversational speech data. For the CSSD evaluation, we also prepare 20 hours of dialogue data for testing purpose, and manually annotate the speaker's timestamps. For the CSSD challenge, we also design a new accuracy evaluation metric to calculate the accuracy of sentence-level speaker diarization. By advancing research on segmentation and clustering techniques for dialogue data, we aim to further promote reproducible research in this field.
Challenge Committee and Support Team
Questions related to the challenge could email email@example.com or firstname.lastname@example.org with the subject of the email titled "Question about the Conversational Short-phrase Speaker Diarization Challenge".
Participants submit inference results, and competition committee will calculate the score. The file format and evaluation metric will be announced in the open stage of the competition.
Three sets of competitors will be awarded first prize, second prize, and third prize. The winners will have the opportunity to participate in ISCSLP 2022 for presentation.
Number of participants: Less than 5 participants per team (including 5 people)
All challengers are welcome to sign up for the competition!