论文标题
用2个以上的演讲者同时讲话，用于说话者识别和诊断的构图嵌入模型
Compositional embedding models for speaker identification and diarization with simultaneous speech from 2+ speakers
论文作者
论文摘要
我们提出了一种新方法来诊断诊断，可以处理与2个人的重叠语音。我们的方法基于组成嵌入[1]：就像标准扬声器嵌入方法（如X-Vector [2]）一样，组成嵌入模型包含一个将语音与不同扬声器分开的函数f。此外，它们还包括一个组成函数g来计算嵌入空间中的设定工具，以推断输入音频中的一组扬声器。在使用合成的LiblisPeech数据进行多人扬声器识别的实验中，所提出的方法优于传统的嵌入方法，这些方法仅受过单独的单个扬声器（不是扬声器集）的训练。在AMI耳机混合物语料库的扬声器诊断实验中，我们达到了最先进的精度（DER = 22.93％），略高于以前的最佳结果（[3]的23.82％）。
We propose a new method for speaker diarization that can handle overlapping speech with 2+ people. Our method is based on compositional embeddings [1]: Like standard speaker embedding methods such as x-vector [2], compositional embedding models contain a function f that separates speech from different speakers. In addition, they include a composition function g to compute set-union operations in the embedding space so as to infer the set of speakers within the input audio. In an experiment on multi-person speaker identification using synthesized LibriSpeech data, the proposed method outperforms traditional embedding methods that are only trained to separate single speakers (not speaker sets). In a speaker diarization experiment on the AMI Headset Mix corpus, we achieve state-of-the-art accuracy (DER=22.93%), slightly higher than the previous best result (23.82% from [3]).
