# Voice Morphing: Two Identities in One Voice

arXiv: https://arxiv.org/abs/2309.02404 · HTML: https://mlsanity.com/paper/2309.02404/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [Efficient Neural Audio Synthesis](https://mlsanity.com/paper/1802.08435/) (2018)
- [Face Morphing Attack Generation & Detection: A Comprehensive Survey](https://mlsanity.com/paper/2011.02045/) (2020)
- [TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context](https://mlsanity.com/paper/2110.04410/) (2021)
- [MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification](https://mlsanity.com/paper/2203.15249/) (2022)
- [Morphing Attack Potential](https://mlsanity.com/paper/2204.13374/) (2022)
- [Master Face Attacks on Face Recognition Systems](https://mlsanity.com/paper/2109.03398/) (2021)
- [DeepVOX: Discovering Features from Raw Audio for Speaker Recognition in Non-ideal Audio Signals](https://mlsanity.com/paper/2008.11668/) (2020)
- [DeepTalk: Vocal Style Encoding for Speaker Recognition and Speech Synthesis](https://mlsanity.com/paper/2012.05084/) (2020)

## Built on by (most cited first, 2 of 2)

- [Scores Know Bobs Voice: Speaker Impersonation Attack](https://mlsanity.com/paper/2603.02781/) (2026)
- [VoxMorph: Scalable Zero-shot Voice Identity Morphing via Disentangled Embeddings](https://mlsanity.com/paper/2601.20883/) (2026)

## Abstract (arXiv)

In a biometric system, each biometric sample or template is typically associated with a single identity. However, recent research has demonstrated the possibility of generating "morph" biometric samples that can successfully match more than a single identity. Morph attacks are now recognized as a potential security threat to biometric systems. However, most morph attacks have been studied on biometric modalities operating in the image domain, such as face, fingerprint, and iris. In this preliminary work, we introduce Voice Identity Morphing (VIM) - a voice-based morph attack that can synthesize speech samples that impersonate the voice characteristics of a pair of individuals. Our experiments evaluate the vulnerabilities of two popular speaker recognition systems, ECAPA-TDNN and x-vector, to VIM, with a success rate (MMPMR) of over 80% at a false match rate of 1% on the Librispeech dataset.