  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.
