Overlap-Aware Diarization: Resegmentation Using Neural End-to-End Overlapped Speech Detection
Overlap-Aware Diarization: Resegmentation Using Neural End-to-End Overlapped Speech Detection
复制标题
重叠感知二值化：使用神经端到端重叠语音检测进行重新分割
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Leibny Paola García
中科院分区：
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作者：
Latané Bullock;H. Bredin;Leibny Paola García
我们解决了在分类系统中有效处理重叠语音的问题。首先，我们详细介绍了一种基于神经长短期记忆的重叠检测架构。其次，结合帧级说话人后验矩阵利用检测到的重叠区域，在重新分割步骤中为重叠帧进行双说话人分配。重叠检测模块在 AMI、DIHARD 和 ETAPE 语料库上实现了最先进的性能。我们在 AMI 上应用了重叠感知重新分段，与基线系统相比，DER 相对减少了 20%。虽然这种方法绝不是重叠感知二值化的最终解决方案，但它揭示了处理重叠的有希望的方向。
We address the problem of effectively handling overlapping speech in a diarization system. First, we detail a neural Long Short-Term Memory- based architecture for overlap detection. Secondly, detected overlap regions are exploited in conjunction with a frame-level speaker posterior matrix to make two-speaker assignments for overlapped frames in the resegmentation step. The overlap detection module achieves state-of-the-art performance on the AMI, DIHARD, and ETAPE corpora. We apply overlap-aware resegmentation on AMI, resulting in a 20% relative DER reduction over the baseline system. While this approach is by no means an end-all solution to overlap-aware diarization, it reveals promising directions for handling overlap.