论文标题
扬声器诊断的在线目标扬声器语音活动检测
Online Target Speaker Voice Activity Detection for Speaker Diarization
论文作者
论文摘要
本文提出了用于扬声器诊断任务的在线目标扬声器语音活动检测系统，该系统不需要基于聚类的诊断系统的先验知识来获取目标扬声器的嵌入。首先，我们采用基于重新连接的前端模型来为每个信号的每个块提取帧级扬声器嵌入。接下来，我们根据这些框架级别的说话者的嵌入和先前估计的目标扬声器嵌入来预测每个说话者的检测状态。然后，通过根据当前块中的预测来汇总这些帧级扬声器嵌入来更新目标扬声器嵌入。我们迭代地提取每个块的结果，然后更新目标扬声器嵌入，直到达到信号末尾。实验结果表明，所提出的方法比在Alimeeting数据集上的基于离线聚类的诊断系统更好。
This paper proposes an online target speaker voice activity detection system for speaker diarization tasks, which does not require a priori knowledge from the clustering-based diarization system to obtain the target speaker embeddings. First, we employ a ResNet-based front-end model to extract the frame-level speaker embeddings for each coming block of a signal. Next, we predict the detection state of each speaker based on these frame-level speaker embeddings and the previously estimated target speaker embedding. Then, the target speaker embeddings are updated by aggregating these frame-level speaker embeddings according to the predictions in the current block. We iteratively extract the results for each block and update the target speaker embedding until reaching the end of the signal. Experimental results show that the proposed method is better than the offline clustering-based diarization system on the AliMeeting dataset.
