Ivan Medennikov, Maxim Korenevsky, Tatiana Prisyach, Yuri Khokhlov, Mariya Korenevskaya, Ivan Sorokin, Tatiana Timofeeva, Anton Mitrofanov, Andrei Andrusenko, I… 展开作者
Ivan Medennikov, Maxim Korenevsky, Tatiana Prisyach, Yuri Khokhlov, Mariya Korenevskaya, Ivan Sorokin, Tatiana Timofeeva, Anton Mitrofanov, Andrei Andrusenko, Ivan Podluzhny, Aleksandr Laptev, Aleksei Romanenko
Speaker diarization for real-life scenarios is an extremely challenging problem. Widely used clustering-based diarization approaches perform rather poorly in such conditions, mainly due to the limited ability to handle overlapping speech. We propose a novel Target-Speaker Voice Activity Detection (TS-VAD) approach, which directly predicts an activity of each speaker on each time frame. TS-VAD model takes conventional speech features (e.g., MFCC) along with i-vectors for each speaker as inputs. A set of binary classification output layers produces activities of each speaker. I-vectors can be estimated iteratively, starting with a strong clustering-based diarization. We also extend the TS-VAD approach to the multi-microphone case using a simple attention mechanism on top of hidden representations extracted from the single-channel TS-VAD model. Moreover, post-processing strategies for the predicted speaker activity probabilities are investigated. Experiments on the CHiME-6 unsegmented data show that TS-VAD achieves state-of-the-art results outperforming the baseline x-vector-based system by more than 30% Diarization Error Rate (DER) abs.
CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings
Johns Hopkins University（约翰斯·霍普金斯大学）；The City University of New York（纽约城市大学）；University of Sheffield（谢菲尔德大学）；Inria（法国国家信息与自动化研究所）；Paderborn University（帕德博恩大学）；Hitachi, Ltd.（日立公司）；Microsoft（微软公司）；Linguistic Data Consortium（语言数据联盟）
The Second DIHARD Diarization Challenge: Dataset, task, and baselines
University of Pennsylvania（宾夕法尼亚大学）；Linguistic Data Consortium（语言数据联盟）；Baidu Research（百度研究院）；PSL University（巴黎文理研究大学）；ENS（巴黎高等师范学院）；EHESS（法国高等社会科学研究院）；CNRS（法国国家科学研究中心）；University of Science and Technology of China（中国科学技术大学）；Indian Institute of Science（印度科学学院）
Linguistic Data Consortium, University of Pennsylvania（宾夕法尼亚大学语言数据联盟）；Indian Institute of Science（印度科学理工学院）；Baidu Research（百度研究院）；University of Science and Technology of China（中国科学技术大学）
G-STAR: End-to-End Global Speaker-Tracking Attributed Recognition
Nanjing University（南京大学）；Shanghai Jiao Tong University（上海交通大学）；Central Media Technology Institute, Huawei（华为中央媒体技术研究院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；ETH Zürich（苏黎世联邦理工学院）
