Speaker embeddings (x-vectors) extracted from very short segments of speech
have recently been shown to give competitive performance in speaker
diarization. We generalize this recipe by extracting from each speech segment,
in parallel with the x-vector, also a diagonal precision matrix, thus providing
a path for the propagation of information about the quality of the speech
segment into a PLDA scoring backend. These precisions quantify the uncertainty
about what the values of the embeddings might have been if they had been
extracted from high quality speech segments. The proposed probabilistic
embeddings (x-vectors with precisions) are interfaced with the PLDA model by
treating the x-vectors as hidden variables and marginalizing them out. We apply
the proposed probabilistic embeddings as input to an agglomerative hierarchical
clustering (AHC) algorithm to do diarization in the DIHARD'19 evaluation set.
We compute the full PLDA likelihood 'by the book' for each clustering
hypothesis that is considered by AHC. We do joint discriminative training of
the PLDA parameters and of the probabilistic x-vector extractor. We demonstrate
accuracy gains relative to a baseline AHC algorithm, applied to traditional
xvectors (without uncertainty), and which uses averaging of binary
log-likelihood-ratios, rather than by-the-book scoring.

利用从短时间内的语音中提取的说话人 embedding（x-vectors）在说话人辨识任务中进行了竞争性表现，本文在该方案中提取了对角精度矩阵，用于衡量声音质量，并构建了概率说话人 embedding，将其作为输入应用于 AHC 算法用于辨识，接口与 PLDA 模型，实现对参数进行联合判别训练，与基础算法相比提高了精度。