# Who is Authentic Speaker

arXiv: https://arxiv.org/abs/2405.00248 · HTML: https://mlsanity.com/paper/2405.00248/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Utterance-level Aggregation For Speaker Recognition In The Wild](https://mlsanity.com/paper/1902.10107/) (2019)
- [Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System](https://mlsanity.com/paper/1804.05160/) (2018)
- [AGAIN-VC: A One-shot Voice Conversion using Activation Guidance and Adaptive Instance Normalization](https://mlsanity.com/paper/2011.00316/) (2020)
- [FragmentVC: Any-to-Any Voice Conversion by End-to-End Extracting and Fusing Fine-Grained Voice Fragments With Attention](https://mlsanity.com/paper/2010.14150/) (2020)
- [The Singing Voice Conversion Challenge 2023](https://mlsanity.com/paper/2306.14422/) (2023)
- [GhostVLAD for set-based face recognition](https://mlsanity.com/paper/1810.09951/) (2018)
- [Singing voice conversion with non-parallel data](https://mlsanity.com/paper/1903.04124/) (2019)
- [Identifying Source Speakers for Voice Conversion based Spoofing Attacks on Speaker Verification Systems](https://mlsanity.com/paper/2206.09103/) (2022)
- [The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection](https://mlsanity.com/paper/2210.02746/) (2022)

## Abstract (arXiv)

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.