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arxiv: 2606.08843 · v2 · pith:675DJ6IKnew · submitted 2026-06-07 · 💻 cs.SD · cs.LG

From A to B to A: Palindromic Zero-Shot Voice Conversion with Non-Parallel Data

Moshe Mandel , Shlomo E. Chazan This is my paper

Pith reviewed 2026-06-30 10:54 UTC · model grok-4.3

classification 💻 cs.SD cs.LG
keywords voice conversionzero-shotnon-parallel dataWavLMKNN retrievalspeaker similaritysynthetic training pairs
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0 comments X

The pith

KNN retrieval over WavLM representations aligns non-parallel speech to create synthetic training pairs for zero-shot voice conversion.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that a supervised voice conversion model can be trained without parallel recordings or language-matched data by using KNN to retrieve matching segments from source and target utterances in WavLM space. The retrieved segments act as synthetic inputs while the actual target audio serves as the output, and a separate speaker verification loss keeps the converted voice consistent with the target speaker. Because the alignment step requires no explicit supervision or parallel corpora, the same English-only training run produces usable conversions in other languages. A sympathetic reader would care because most existing voice conversion systems still demand either paired utterances or large amounts of target-language data.

Core claim

The central claim is that KNN retrieval over WavLM representations produces sufficiently accurate alignments between non-parallel source and target segments to serve as synthetic training pairs; a model trained on these pairs plus a speaker-verification loss then performs zero-shot voice conversion that maintains high naturalness and target-speaker similarity across languages even when all training data is English.

What carries the argument

KNN retrieval over WavLM representations to form synthetic-to-real training pairs from non-parallel utterances.

If this is right

  • The same English-only model can be applied directly to target speakers in other languages without retraining or parallel data.
  • No explicit time-alignment or phonetic transcription is required to build the training set.
  • The speaker loss derived from a pretrained verification model is sufficient to enforce target identity on the converted output.
  • The synthetic-to-real training paradigm removes the need for any parallel corpus while still supporting supervised learning.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the KNN alignments prove robust, the same retrieval step could be reused to bootstrap training data for other audio-to-audio tasks that currently lack parallel corpora.
  • The method implicitly assumes that WavLM space preserves enough phonetic and prosodic detail for segment matching; relaxing this assumption would require testing alternative self-supervised encoders.
  • Because the approach separates the alignment stage from the conversion network, either component could be swapped for newer representations or architectures without redesigning the overall pipeline.

Load-bearing premise

KNN retrieval over WavLM representations produces alignments accurate enough to yield useful synthetic training pairs.

What would settle it

A controlled listening test in which the proposed system is compared head-to-head with a parallel-data baseline on the same target speakers and the proposed system scores reliably lower on both naturalness and speaker similarity would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.08843 by Moshe Mandel, Shlomo E. Chazan.

Figure 1
Figure 1. Figure 1: Overview of our palindromic voice conversion scheme. During training we synthesize input to the model by utilizing offline voice conversion via KNN of WavLM features. We optimize the latent and waveform outputs Ae1 and ea1 against their supervised real counterparts, using a collection of recon￾struction, adversarial and speaker losses. We generalize to real input at inference time. See project page for int… view at source ↗
Figure 2
Figure 2. Figure 2: Effect of prompt duration across multiple languages. Speaker Similarity (↑) is shown on the left, WER (↓) in the center, and DNS-MOS (↑) on the right. Our method achieves the best WER while remaining comparable to other approaches in terms of speaker similarity and DNS-MOS. ments in WER across all prompt duration settings, indicating strong cross-lingual generalization. Furthermore, speaker simi￾larity and… view at source ↗
read the original abstract

We present a voice conversion (VC) framework that utilizes K-Nearest Neighbors (KNN) retrieval over WavLM representations to align non-parallel source and target speech, constructing synthetic training pairs for supervised learning. The retrieved segments serve as synthetic inputs, while real target audio provides ground-truth outputs, forming a synthetic-to-real training paradigm that naturally supports multilingual data without requiring parallel corpora or explicit alignment. To ensure consistent target-speaker identity, we incorporate a speaker loss derived from a pretrained speaker verification model. Experiments across multiple languages demonstrate that the proposed approach achieves high naturalness and strong speaker similarity, outperforming competitive VC baselines, despite being trained exclusively on English data. Samples can be accessed at: https://palindromic-vc.github.io.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents a zero-shot voice conversion framework that employs KNN retrieval over WavLM features to align non-parallel source and target utterances, thereby synthesizing training pairs for a supervised model. Real target audio serves as the ground-truth output while the retrieved segments act as inputs; a speaker-verification loss is added to preserve target identity. The model is trained exclusively on English data yet is reported to generalize to multiple languages, achieving higher naturalness and speaker similarity than competitive baselines.

Significance. If the KNN-derived pairs are verifiably content-aligned, the synthetic-to-real paradigm would offer a practical route to non-parallel, cross-lingual VC without language-specific resources or parallel corpora. The explicit use of off-the-shelf pretrained models (WavLM, speaker verifier) and the public release of listening samples constitute reproducible strengths that facilitate external validation.

major comments (2)
  1. [Method] Method section (KNN retrieval paragraph): no quantitative metric (e.g., forced-alignment phone error rate, cosine similarity of content embeddings, or human content-match rating) is reported for the retrieved segments when source and target languages differ. Because the entire supervised objective rests on these pairs being phonetically matched, absence of such validation directly undermines the cross-lingual generalization claim.
  2. [Experiments] Experiments section: while the abstract asserts outperformance over baselines, the manuscript provides neither per-language objective scores (e.g., WER, speaker similarity cosine), error bars, nor an ablation that replaces KNN retrieval with random or language-mismatched pairs. Without these controls it is impossible to determine whether performance gains derive from the proposed alignment or from other factors.
minor comments (2)
  1. [Introduction] The title uses “Palindromic” without an explicit definition or diagram showing the A→B→A cycle; a short clarifying sentence or figure would aid readers.
  2. [Method] The speaker-loss formulation is described only at a high level; the precise weighting hyper-parameter and its interaction with the reconstruction loss should be stated explicitly.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and indicate planned revisions.

read point-by-point responses

  1. Referee: [Method] Method section (KNN retrieval paragraph): no quantitative metric (e.g., forced-alignment phone error rate, cosine similarity of content embeddings, or human content-match rating) is reported for the retrieved segments when source and target languages differ. Because the entire supervised objective rests on these pairs being phonetically matched, absence of such validation directly undermines the cross-lingual generalization claim.

    Authors: We agree that the manuscript does not report quantitative metrics validating phonetic alignment of KNN-retrieved segments in cross-lingual cases. While the approach builds on WavLM's established content modeling capabilities, direct evidence would strengthen the claims. In revision we will add cosine similarity between content embeddings of source and retrieved segments, computed separately for intra- and cross-lingual pairs, and include these results in the method section. revision: yes

  2. Referee: [Experiments] Experiments section: while the abstract asserts outperformance over baselines, the manuscript provides neither per-language objective scores (e.g., WER, speaker similarity cosine), error bars, nor an ablation that replaces KNN retrieval with random or language-mismatched pairs. Without these controls it is impossible to determine whether performance gains derive from the proposed alignment or from other factors.

    Authors: We acknowledge that the current experiments section lacks per-language breakdowns, error bars, and the requested ablation. In the revised manuscript we will report per-language WER and speaker-similarity cosine scores with error bars from repeated runs. We will also add an ablation replacing KNN retrieval with random and language-mismatched pairing to isolate the contribution of the alignment step. revision: yes

Circularity Check

0 steps flagged

No circularity: method relies on external pretrained models and experimental validation

full rationale

The paper's core pipeline constructs synthetic training pairs via KNN retrieval on WavLM features from non-parallel data, then applies supervised training with an external speaker verification loss; performance claims rest on multilingual experiments rather than any internal derivation, fitted parameter renamed as prediction, or self-citation chain. No self-definitional steps, ansatzes smuggled via prior work, or uniqueness theorems imported from the authors appear in the description. The approach is self-contained against external benchmarks (WavLM, speaker verifier) with no reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the method implicitly assumes WavLM features are suitable for alignment and that the speaker verification model provides reliable identity supervision.

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Reference graph

Works this paper leans on

35 extracted references · 5 canonical work pages · 2 internal anchors

  1. [1]

    From A to B to A: Palindromic Zero-Shot Voice Conversion with Non-Parallel Data

    Introduction V oice conversion (VC) aims to modify the speech of a source speaker to match the characteristics of a target speaker while preserving the linguistic content. These characteristics may in- clude speaker identity, prosody, or emotional style. In this work, we address zero-shot, any-to-any speaker identity conversion with unseen source and targ...

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  2. [2]

    We intro- duce an end-to-end training framework for any-to-any zero-shot voice conversion under a non-parallel data setting

    Method An overview of our method is shown in Figure 1. We intro- duce an end-to-end training framework for any-to-any zero-shot voice conversion under a non-parallel data setting. The core motivation of this design is to enable supervised learning of speaker conversion without requiring parallel or aligned speech, by introducing a synthetic intermediate r...

    work page arXiv

  3. [3]

    In Stage 2, we train a 77M-parameter six-layer Transformer with 16 attention heads and a hidden di- mension of 1024 for 800K steps using Adam [25] with a learn- ing rate of3×10 −4

    Experiments In Stage 1, following KNN-VC [18], we extract features from the 6th layer of WavLM [20] and train a 16M-parameter vocoder for 100K steps. In Stage 2, we train a 77M-parameter six-layer Transformer with 16 attention heads and a hidden di- mension of 1024 for 800K steps using Adam [25] with a learn- ing rate of3×10 −4. In Stage 3, a new instance...

  4. [4]

    Conclusions & Future Work We present a non-parallel, zero-shot, any-to-any voice con- version framework that enables supervised speaker conversion without aligned speech. By introducing a palindromic training strategy based on controlled KNN-generated synthetic features, our method leverages non-parallel data to learn speaker identity mapping in a scalabl...

  5. [5]

    It was not used to generate substantial technical content, research ideas, experimental design, or results

    Generative AI Use Disclosure A generative AI tool was used to assist with language editing and refinement of specific sections of this manuscript. It was not used to generate substantial technical content, research ideas, experimental design, or results

  6. [6]

    V oice conver- sion using deep neural networks with layer-wise generative train- ing,

    L.-H. Chen, Z.-H. Ling, L.-J. Liu, and L.-R. Dai, “V oice conver- sion using deep neural networks with layer-wise generative train- ing,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 12, pp. 1859–1872, 2014

    2014

  7. [7]

    V oice conversion using deep bidirectional long short-term memory based recurrent neural networks,

    L. Sun, S. Kang, K. Li, and H. Meng, “V oice conversion using deep bidirectional long short-term memory based recurrent neural networks,” inInternational conference on acoustics, speech and signal processing (ICASSP). IEEE, 2015

    2015

  8. [8]

    Phonetic poste- riorgrams for many-to-one voice conversion without parallel data training,

    L. Sun, K. Li, H. Wang, S. Kang, and H. Meng, “Phonetic poste- riorgrams for many-to-one voice conversion without parallel data training,” inInternational Conference on Multimedia and Expo (ICME). IEEE, 2016

    2016

  9. [9]

    Any- to-many voice conversion with location-relative sequence-to- sequence modeling,

    S. Liu, Y . Cao, D. Wang, X. Wu, X. Liu, and H. Meng, “Any- to-many voice conversion with location-relative sequence-to- sequence modeling,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 1717–1728, 2021

    2021

  10. [10]

    Transfer learning from speech synthesis to voice conversion with non-parallel training data,

    M. Zhang, Y . Zhou, L. Zhao, and H. Li, “Transfer learning from speech synthesis to voice conversion with non-parallel training data,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 1290–1302, 2021

    2021

  11. [11]

    Cotatron: Transcription- guided speech encoder for any-to-many voice conversion without parallel data,

    S.-w. Park, D.-y. Kim, and M.-c. Joe, “Cotatron: Transcription- guided speech encoder for any-to-many voice conversion without parallel data,”Proc. Interspeech, 2020

    2020

  12. [12]

    Autovc: Zero-shot voice style transfer with only au- toencoder loss,

    K. Qian, Y . Zhang, S. Chang, X. Yang, and M. Hasegawa- Johnson, “Autovc: Zero-shot voice style transfer with only au- toencoder loss,” inInternational Conference on Machine Learn- ing. PMLR, 2019

    2019

  13. [13]

    One-shot voice conversion by separating speaker and content representations with instance normalization,

    J.-c. Chou, C.-c. Yeh, and H.-y. Lee, “One-shot voice conversion by separating speaker and content representations with instance normalization,”Proc. Interspeech, 2019

    2019

  14. [14]

    Speech Resynthesis from Discrete Disentangled Self-Supervised Representations,

    A. Polyak, Y . Adi, J. Copet, E. Kharitonov, K. Lakhotia, W.-N. Hsu, A. Mohamed, and E. Dupoux, “Speech Resynthesis from Discrete Disentangled Self-Supervised Representations,” inProc. Interspeech, 2021

    2021

  15. [15]

    Frag- mentvc: Any-to-any voice conversion by end-to-end extracting and fusing fine-grained voice fragments with attention,

    Y . Y . Lin, C.-M. Chien, J.-H. Lin, H.-y. Lee, and L.-s. Lee, “Frag- mentvc: Any-to-any voice conversion by end-to-end extracting and fusing fine-grained voice fragments with attention,” inInter- national Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021

    2021

  16. [16]

    S3prl-vc: Open-source voice conversion frame- work with self-supervised speech representations,

    W.-C. Huang, S.-W. Yang, T. Hayashi, H.-Y . Lee, S. Watanabe, and T. Toda, “S3prl-vc: Open-source voice conversion frame- work with self-supervised speech representations,” inInterna- tional Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022

    2022

  17. [17]

    Freevc: Towards high-quality text- free one-shot voice conversion,

    J. Li, W. Tu, and L. Xiao, “Freevc: Towards high-quality text- free one-shot voice conversion,” inInternational Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023

    2023

  18. [18]

    Vqvc+: One-shot voice conversion by vector quantization and u-net architecture,

    D.-Y . Wu, Y .-H. Chen, and H.-Y . Lee, “Vqvc+: One-shot voice conversion by vector quantization and u-net architecture,”Proc. Interspeech, 2020

    2020

  19. [19]

    Again-vc: A one-shot voice conversion using activation guidance and adaptive instance normalization,

    Y .-H. Chen, D.-Y . Wu, T.-H. Wu, and H.-y. Lee, “Again-vc: A one-shot voice conversion using activation guidance and adaptive instance normalization,” inInternational Conference on Acous- tics, Speech and Signal Processing (ICASSP). IEEE, 2021

    2021

  20. [20]

    O o-vc: Synthetic data-driven one-to-one alignment for any-to- any voice conversion,

    H. T. Tu, H. Vu, N. T. Cuong, N. D. Hy, and N. T. T. Trang, “O o-vc: Synthetic data-driven one-to-one alignment for any-to- any voice conversion,” inFindings of the Association for Compu- tational Linguistics: EMNLP 2025, 2025, pp. 16 197–16 208

    2025

  21. [21]

    arXiv preprint arXiv:2411.09943 , year=

    S. Liu, “Zero-shot voice conversion with diffusion transformers,” arXiv preprint arXiv:2411.09943, 2024

    work page arXiv 2024

  22. [22]

    Synthvc: Leveraging syn- thetic data for end-to-end low latency streaming voice conver- sion,

    Z. Guo, Z. Ning, G. Ma, and L. Xie, “Synthvc: Leveraging syn- thetic data for end-to-end low latency streaming voice conver- sion,”arXiv preprint arXiv:2510.09245, 2025

    work page arXiv 2025

  23. [23]

    V oice conversion with just nearest neighbors,

    M. Baas, B. van Niekerk, and H. Kamper, “V oice conversion with just nearest neighbors,” inProc. Interspeech, 2023

    2023

  24. [24]

    Phoneme hallucina- tor: One-shot voice conversion via set expansion,

    S. Shan, Y . Li, A. Banerjee, and J. B. Oliva, “Phoneme hallucina- tor: One-shot voice conversion via set expansion,” inProceedings of the AAAI Conference on Artificial Intelligence, 2024

    2024

  25. [25]

    Wavlm: Large-scale self- supervised pre-training for full stack speech processing,

    S. Chen, C. Wang, Z. Chen, Y . Wu, S. Liu, Z. Chen, J. Li, N. Kanda, T. Yoshioka, X. Xiaoet al., “Wavlm: Large-scale self- supervised pre-training for full stack speech processing,”IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 6, pp. 1505–1518, 2022

    2022

  26. [26]

    Hifi-gan: Generative adversarial net- works for efficient and high fidelity speech synthesis,

    J. Kong, J. Kim, and J. Bae, “Hifi-gan: Generative adversarial net- works for efficient and high fidelity speech synthesis,”Advances in neural information processing systems, vol. 33, pp. 17 022– 17 033, 2020

    2020

  27. [27]

    Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification,

    B. Desplanques, J. Thienpondt, and K. Demuynck, “Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification,”Proc. Interspeech, 2020

    2020

  28. [28]

    Vevo: Controllable zero-shot voice imitation with self- supervised disentanglement,

    X. Zhang, X. Zhang, K. Peng, Z. Tang, V . Manohar, Y . Liu, J. Hwang, D. Li, Y . Wang, J. Chan, Y . Huang, Z. Wu, and M. Ma, “Vevo: Controllable zero-shot voice imitation with self- supervised disentanglement,” inICLR, 2025

    2025

  29. [29]

    Parallel wavegan: A fast waveform generation model based on generative adversarial net- works with multi-resolution spectrogram,

    R. Yamamoto, E. Song, and J.-M. Kim, “Parallel wavegan: A fast waveform generation model based on generative adversarial net- works with multi-resolution spectrogram,” inInternational Con- ference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020

    2020

  30. [30]

    Adam: A method for stochastic opti- mization,

    D. P. Kingma and J. Ba, “Adam: A method for stochastic opti- mization,” inInternational Conference on Learning Representa- tions (ICLR), 2015

    2015

  31. [31]

    Lib- rispeech: an asr corpus based on public domain audio books,

    V . Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Lib- rispeech: an asr corpus based on public domain audio books,” inInternational conference on acoustics, speech and signal pro- cessing (ICASSP). IEEE, 2015

    2015

  32. [32]

    Mls: A large-scale multilingual dataset for speech research,

    V . Pratap, Q. Xu, A. Sriram, G. Synnaeve, and R. Collobert, “Mls: A large-scale multilingual dataset for speech research,”Proc. In- terspeech, 2020

    2020

  33. [33]

    Dnsmos p.835: A non- intrusive perceptual objective speech quality metric to evalu- ate noise suppressors,

    C. K. Reddy, V . Gopal, and R. Cutler, “Dnsmos p.835: A non- intrusive perceptual objective speech quality metric to evalu- ate noise suppressors,” inInternational conference on acoustics, speech and signal processing (ICASSP). IEEE, 2022

    2022

  34. [34]

    Reshape Dimensions Network for Speaker Recognition,

    I. Yakovlev, R. Makarov, A. Balykin, P. Malov, A. Okhotnikov, and N. Torgashov, “Reshape Dimensions Network for Speaker Recognition,” inProc. Interspeech, 2024

    2024

  35. [35]

    Robust Speech Recognition via Large-Scale Weak Supervision

    A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large- scale weak supervision,” 2022. [Online]. Available: https: //arxiv.org/abs/2212.04356

    work page internal anchor Pith review Pith/arXiv arXiv 2022