Voice conversion (VC) using deep learning technologies can now generate high
quality one-to-many voices and thus has been used in some practical application
fields, such as entertainment and healthcare. However, voice conversion can
pose potential social issues when manipulated voices are employed for deceptive
purposes. Moreover, it is a big challenge to find who are real speakers from
the converted voices as the acoustic characteristics of source speakers are
changed greatly. In this paper we attempt to explore the feasibility of
identifying authentic speakers from converted voices. This study is conducted
with the assumption that certain information from the source speakers persists,
even when their voices undergo conversion into different target voices.
Therefore our experiments are geared towards recognising the source speakers
given the converted voices, which are generated by using FragmentVC on the
randomly paired utterances from source and target speakers. To improve the
robustness against converted voices, our recognition model is constructed by
using hierarchical vector of locally aggregated descriptors (VLAD) in deep
neural networks. The authentic speaker recognition system is mainly tested in
two aspects, including the impact of quality of converted voices and the
variations of VLAD. The dataset used in this work is VCTK corpus, where source
and target speakers are randomly paired. The results obtained on the converted
utterances show promising performances in recognising authentic speakers from
converted voices.

探索利用深度学习技术进行声音转换，识别原始说话者的可行性，通过使用分段转换的转换语音进行实验证明从转换声音中识别真实说话者的可行性，并使用具有来自源说话者的某些信息的 VLAD 进行构建的识别模型在转换语音上表现出很好的性能。

谁是真实的讲者

Who is Authentic Speaker

The objective of this paper is to design an embedding method that maps local
features describing an image (e.g. SIFT) to a higher dimensional representation
useful for the image retrieval problem. First, motivated by the relationship
between the linear approximation of a nonlinear function in high dimensional
space and the stateof-the-art feature representation used in image retrieval,
i.e., VLAD, we propose a new approach for the approximation. The embedded
vectors resulted by the function approximation process are then aggregated to
form a single representation for image retrieval. Second, in order to make the
proposed embedding method applicable to large scale problem, we further derive
its fast version in which the embedded vectors can be efficiently computed,
i.e., in the closed-form. We compare the proposed embedding methods with the
state of the art in the context of image search under various settings: when
the images are represented by medium length vectors, short vectors, or binary
vectors. The experimental results show that the proposed embedding methods
outperform existing the state of the art on the standard public image retrieval
benchmarks.

本文旨在设计一种嵌入方法，将描述图像的局部特征 (如 SIFT) 映射到一个更高维的表示中，以解决图像检索问题。我们提出了一种基于函数逼近的新方法，并将逼近得到的嵌入向量聚合成单个表示，以便于图像检索。此外，我们还提出了快速版本的嵌入方法，并在标准公共图像检索基准下分别对嵌入方法和其它方法进行了实验比较，结果表明嵌入方法优于现有的图像检索算法。