Spoofing detection is today a mainstream research topic. Standard metrics can
be applied to evaluate the performance of isolated spoofing detection solutions
and others have been proposed to support their evaluation when they are
combined with speaker detection. These either have well-known deficiencies or
restrict the architectural approach to combine speaker and spoof detectors. In
this paper, we propose an architecture-agnostic detection cost function
(a-DCF). A generalisation of the original DCF used widely for the assessment of
automatic speaker verification (ASV), the a-DCF is designed for the evaluation
of spoofing-robust ASV. Like the DCF, the a-DCF reflects the cost of decisions
in a Bayes risk sense, with explicitly defined class priors and detection cost
model. We demonstrate the merit of the a-DCF through the benchmarking
evaluation of architecturally-heterogeneous spoofing-robust ASV solutions.

提出了一种适用于欺骗性溯源的 ASV 评估的通用检测成本函数（a-DCF），通过基于架构异构的欺骗性溯源固定 ASV 解决方案的基准评估，证明了 a-DCF 的优点。

a-DCF：一种与架构无关的度量标准，适用于抗欺诈的说话人验证

a-DCF: an architecture agnostic metric with application to  spoofing-robust speaker verification

Recent approaches to automatically detect the speaker of an utterance of
direct speech often disregard general information about characters in favor of
local information found in the context, such as surrounding mentions of
entities. In this work, we explore stylistic representations of characters
built by encoding their quotes with off-the-shelf pretrained Authorship
Verification models in a large corpus of English novels (the Project Dialogism
Novel Corpus). Results suggest that the combination of stylistic and topical
information captured in some of these models accurately distinguish characters
among each other, but does not necessarily improve over semantic-only models
when attributing quotes. However, these results vary across novels and more
investigation of stylometric models particularly tailored for literary texts
and the study of characters should be conducted.

通过对大量英语小说进行预训练的作者验证模型编码以角色的引用，本研究探索了角色的文体表征，并且结果表明这些模型中融合的文体和主题信息能够准确区分角色，但在归属引用方面并没有必要超过仅有语义的模型，然而这些结果在小说之间存在差异，因此在文学文本和角色研究领域需要进行更多针对性的文体测量模型的研究。

区分虚构声音：引语归属的作者验证模型研究

Distinguishing Fictional Voices: a Study of Authorship Verification  Models for Quotation Attribution

In this paper we propose a method of single-channel speaker-independent
multi-speaker speech separation for an unknown number of speakers. As opposed
to previous works, in which the number of speakers is assumed to be known in
advance and speech separation models are specific for the number of speakers,
our proposed method can be applied to cases with different numbers of speakers
using a single model by recursively separating a speaker. To make the
separation model recursively applicable, we propose one-and-rest permutation
invariant training (OR-PIT). Evaluation on WSJ0-2mix and WSJ0-3mix datasets
show that our proposed method achieves state-of-the-art results for two- and
three-speaker mixtures with a single model. Moreover, the same model can
separate four-speaker mixture, which was never seen during the training. We
further propose the detection of the number of speakers in a mixture during
recursive separation and show that this approach can more accurately estimate
the number of speakers than detection in advance by using a deep neural network
based classifier.

该论文提出了一种用于未知扬声器数量的单通道扬声器非特定多扬声器语音分离方法，其中引入了排列不变训练，使模型具有递归性，并且通过使用基于深度神经网络的分类器进行递归分离来更准确地估计混音中扬声器数量，从而在 WSJ0-2mix 和 WSJ0-3mix 数据集上实现了最先进的结果，具有可扩展性和准确性。