论文标题
分析武器挑战的但诊断系统
Analysis of the BUT Diarization System for VoxConverse Challenge
论文作者
论文摘要
本文介绍了But Team开发的系统为Voxceleb扬声器识别挑战的第四轨开发的系统，重点是在VoxConverse数据集上进行诊断。该系统包括信号预处理，语音活动检测，扬声器嵌入提取的扬声器，最初的凝聚性分层聚类，然后使用贝叶斯隐藏的马尔可夫模型诊断，这是基于每个口号的全球嵌入式嵌入式的重群落步骤，以及重叠的语音检测和处理和处理。我们为每个步骤提供比较，并分享系统中最相关的模块的实现。我们的系统在挑战中以初级度量（诊断错误率）为第二，首先根据次级度量标准（JACCARD错误率）得分。
This paper describes the system developed by the BUT team for the fourth track of the VoxCeleb Speaker Recognition Challenge, focusing on diarization on the VoxConverse dataset. The system consists of signal pre-processing, voice activity detection, speaker embedding extraction, an initial agglomerative hierarchical clustering followed by diarization using a Bayesian hidden Markov model, a reclustering step based on per-speaker global embeddings and overlapped speech detection and handling. We provide comparisons for each of the steps and share the implementation of the most relevant modules of our system. Our system scored second in the challenge in terms of the primary metric (diarization error rate) and first according to the secondary metric (Jaccard error rate).
