speaker diarization中x向量序列（VBx）的贝叶斯HMM聚类：理论、实现及标准任务分析
Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks
摘要：
最近提出的VBx diarization方法使用贝叶斯隐马尔可夫模型，以找到扬声器集群的序列中的x向量。在这项工作中，我们进行了广泛的VBx日记与文献中的其他方法的性能比较，我们表明，VBx实现了卓越的性能上的三个最流行的数据集，用于评估日记：CALLHOME，AMI和DIHARDII数据集。此外，我们提出了第一次的VBx模型的推导和更新公式，专注于该模型的效率和简单性相比，以前的和更复杂的BHMM模型工作在逐帧的标准倒谱特征。与本出版物一起，我们发布了在宽和窄带数据上训练我们实验中使用的x向量提取器的配方，以及在所有三个数据集上获得最先进性能的VBx配方。此外，我们指出了缺乏一个标准化的评估协议的AMI数据集，我们提出了一个新的协议，波束成形和混合耳机音频的基础上，官方AMI分区和transparency。
英文摘要：
The recently proposed VBx diarization method uses a Bayesian hidden Markov model to find speaker clusters in a sequence of x-vectors. In this work we perform an extensive comparison of performance of the VBx diarization with other approaches in the literature and we show that VBx achieves superior performance on three of the most popular datasets for evaluating diarization: CALLHOME, AMI and DIHARDII datasets. Further, we present for the first time the derivation and update formulae for the VBx model, focusing on the efficiency and simplicity of this model as compared to the previous and more complex BHMM model working on frame-by-frame standard Cepstral features. Together with this publication, we release the recipe for training the x-vector extractors used in our experiments on both wide and narrowband data, and the VBx recipes that attain state-of-the-art performance on all three datasets. Besides, we point out the lack of a standardized evaluation protocol for AMI dataset and we propose a new protocol for both Beamformed and Mix-Headset audios based on the official AMI partitions and transcriptions.
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