# TriAAN-VC: Triple Adaptive Attention Normalization for Any-to-Any Voice Conversion

arXiv: https://arxiv.org/abs/2303.09057 · HTML: https://mlsanity.com/paper/2303.09057/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2023.

## Built on by (most cited first, 10 of 10)

- [Prosody-Adaptable Audio Codecs for Zero-Shot Voice Conversion via In-Context Learning](https://mlsanity.com/paper/2505.15402/) (2025)
- [DEX-TTS: Diffusion-based EXpressive Text-to-Speech with Style Modeling on Time Variability](https://mlsanity.com/paper/2406.19135/) (2024)
- [Can Diffusion Models Disentangle? A Theoretical Perspective](https://mlsanity.com/paper/2504.00220/) (2025)
- [Emotion Recognition and Generation: A Comprehensive Review of Face, Speech, and Text Modalities](https://mlsanity.com/paper/2502.06803/) (2025)
- [NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion](https://mlsanity.com/paper/2511.00256/) (2025)
- [EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events](https://mlsanity.com/paper/2309.01480/) (2023)
- [D-CAPTCHA++: A Study of Resilience of Deepfake CAPTCHA under Transferable Imperceptible Adversarial Attack](https://mlsanity.com/paper/2409.07390/) (2024)
- [Discrete Unit based Masking for Improving Disentanglement in Voice Conversion](https://mlsanity.com/paper/2409.11560/) (2024)
- [StarVC: A Unified Auto-Regressive Framework for Joint Text and Speech Generation in Voice Conversion](https://mlsanity.com/paper/2506.02414/) (2025)
- [DAST: A Dual-Stream Voice Anonymization Attacker with Staged Training](https://mlsanity.com/paper/2603.12840/) (2026)

## Abstract (arXiv)

Voice Conversion (VC) must be achieved while maintaining the content of the source speech and representing the characteristics of the target speaker. The existing methods do not simultaneously satisfy the above two aspects of VC, and their conversion outputs suffer from a trade-off problem between maintaining source contents and target characteristics. In this study, we propose Triple Adaptive Attention Normalization VC (TriAAN-VC), comprising an encoder-decoder and an attention-based adaptive normalization block, that can be applied to non-parallel any-to-any VC. The proposed adaptive normalization block extracts target speaker representations and achieves conversion while minimizing the loss of the source content with siamese loss. We evaluated TriAAN-VC on the VCTK dataset in terms of the maintenance of the source content and target speaker similarity. Experimental results for one-shot VC suggest that TriAAN-VC achieves state-of-the-art performance while mitigating the trade-off problem encountered in the existing VC methods.