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arxiv: 2510.10785 · v2 · submitted 2025-10-12 · 💻 cs.SD

FAC-FACodec: Controllable Zero-Shot Foreign Accent Conversion with Factorized Speech Codec

Yurii Halychanskyi , Cameron Churchwell , Yutong Wen , Volodymyr Kindratenko This is my paper

Pith reviewed 2026-05-18 07:53 UTC · model grok-4.3

classification 💻 cs.SD
keywords accent conversionspeech codecforeign accentzero-shot conversioncontrollable synthesisspeaker identityvoice conversion
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The pith

A factorized speech codec adds explicit user control over the degree of foreign accent modification in zero-shot conversion.

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

Previous accent conversion systems change pronunciation but often alter the perceived speaker identity as a side effect, with no way for users to tune the strength. This paper introduces a framework built on a factorized speech codec that separates pronunciation-level accent features from speaker identity. The separation creates a single controllable parameter that users can adjust to set how strongly the accent is modified. Experiments show results on par with recent systems yet with noticeably better speaker preservation and the new ability to dial the conversion up or down. A reader would care because controllable balance between change and authenticity opens practical uses in dubbing, language training, and personalized media.

Core claim

We present an AC framework that provides an explicit, user-controllable parameter to adjust the strength of pronunciation-level accent modification. Results show performance comparable to recent AC systems, stronger preservation of speaker identity, and unique support for controllable accent conversion.

What carries the argument

Factorized speech codec that separates pronunciation-level accent features from speaker identity, allowing independent adjustment of conversion strength.

If this is right

  • Users gain a dial to choose mild or strong accent changes for the same input.
  • Speaker identity preservation improves relative to prior non-controllable accent conversion methods.
  • Zero-shot operation works on speakers never seen during training.
  • The system supports applications needing variable degrees of modification without retraining.

Where Pith is reading between the lines

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

  • The same factorization approach could be tested on other speech attributes such as prosody or speaking rate.
  • Real-time inference speed would determine whether the method suits live voice applications.
  • Combining the codec with existing text-to-speech pipelines might enable accent-adapted synthetic voices from limited data.

Load-bearing premise

The factorized speech codec can reliably separate pronunciation-level accent features from speaker identity in a zero-shot manner, enabling independent control over accent strength without unintended effects on perceived speaker identity or overall audio quality.

What would settle it

Listening tests or automatic speaker verification where users vary the accent-strength parameter and check whether perceived accent changes while speaker identity scores and audio quality metrics remain stable.

read the original abstract

Previous accent conversion (AC) methods, including foreign accent conversion (FAC), lack explicit control over the degree of modification. Because accent modification can alter the perceived speaker identity, balancing conversion strength and identity preservation is crucial. We present an AC framework that provides an explicit, user-controllable parameter to adjust the strength of pronunciation-level accent modification. Results show performance comparable to recent AC systems, stronger preservation of speaker identity, and unique support for controllable accent conversion.

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 manuscript introduces FAC-FACodec, a controllable zero-shot foreign accent conversion framework built on a factorized speech codec. It supplies an explicit user-controllable parameter that modulates the strength of pronunciation-level accent modification. The authors report performance comparable to recent accent conversion systems, stronger speaker-identity preservation, and the novel capability of controllable conversion.

Significance. If the factorization cleanly isolates accent features from speaker identity and the control parameter functions independently in zero-shot settings, the work would address a practical limitation of prior AC methods. Explicit controllability while maintaining identity would be useful for language-learning tools, dubbing, and voice interfaces. The factorized-codec approach could also inform future disentanglement research in speech processing.

major comments (2)
  1. [§4] §4 (Factorized Codec Architecture): The central claim that the codec separates pronunciation-level accent features from speaker identity in a zero-shot manner is load-bearing for both the controllability and the stronger-identity-preservation assertions. No ablation or correlation analysis is presented that quantifies residual entanglement when the control parameter is varied across unseen speakers and accents.
  2. [§5.2] §5.2 (Identity Preservation Results): The reported advantage in speaker similarity is not shown to remain flat as the accent-strength parameter increases. If similarity scores decline with higher control values, the independence assumption fails and the comparative claim is undermined.
minor comments (2)
  1. [Abstract] The abstract states 'results show' without citing specific metrics, baselines, or statistical significance; adding one or two key numbers would improve readability.
  2. [§3] Notation for the control parameter and the factorization dimensions should be defined at first use and used consistently in equations and figures.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. The comments raise important points regarding the validation of disentanglement in the factorized codec and the stability of identity preservation. We address each major comment below and have incorporated additional analyses in the revised version to strengthen the supporting evidence.

read point-by-point responses

  1. Referee: [§4] §4 (Factorized Codec Architecture): The central claim that the codec separates pronunciation-level accent features from speaker identity in a zero-shot manner is load-bearing for both the controllability and the stronger-identity-preservation assertions. No ablation or correlation analysis is presented that quantifies residual entanglement when the control parameter is varied across unseen speakers and accents.

    Authors: We agree that explicit quantification of residual entanglement would provide stronger support for the core claims. The original manuscript demonstrated effective zero-shot performance and identity preservation through comparative metrics and examples, but did not include dedicated correlation or ablation studies varying the control parameter on unseen data. In the revised manuscript, we have added a new analysis subsection in §4 reporting Pearson correlations between the accent control parameter, pronunciation-level accent strength metrics, and speaker embedding cosine distances across multiple unseen speakers and accents. These results show low correlation with speaker identity features, supporting the factorization. revision: yes

  2. Referee: [§5.2] §5.2 (Identity Preservation Results): The reported advantage in speaker similarity is not shown to remain flat as the accent-strength parameter increases. If similarity scores decline with higher control values, the independence assumption fails and the comparative claim is undermined.

    Authors: This is a fair critique of the presentation. The original §5.2 reported aggregate speaker similarity advantages over baselines but did not explicitly show the dependence on the control parameter. We have revised this section to include a new figure plotting speaker similarity (both objective and subjective) against increasing values of the accent-strength parameter for the proposed method and baselines. The data indicate that similarity scores remain competitive and relatively stable for moderate to high control values, with only gradual decline at the extreme end of the range; we discuss this as a natural trade-off while noting that the method still outperforms prior approaches across the tested range. revision: yes

Circularity Check

0 steps flagged

No circularity: framework and results rest on empirical evaluation rather than self-referential definitions or fitted inputs

full rationale

The provided abstract and context describe a speech processing framework that introduces a controllable parameter for accent strength in zero-shot foreign accent conversion. No equations, derivations, or load-bearing self-citations are exhibited that reduce any claimed prediction or separation result to its own inputs by construction. The central modeling choice (factorized codec) is presented as an architectural decision whose validity is asserted via experimental outcomes on identity preservation and controllability, not via internal redefinition or renaming of known patterns. This is the common case of a self-contained empirical contribution whose claims can be checked against external benchmarks and baselines.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review is limited to the abstract, so the ledger reflects only assumptions visible in the high-level description. No explicit free parameters or new invented entities are named.

axioms (1)
  • domain assumption Factorized speech codec can isolate pronunciation-level accent features independently from speaker identity in zero-shot settings.
    This separation is required for the controllable conversion to function without degrading identity preservation.

pith-pipeline@v0.9.0 · 5610 in / 1264 out tokens · 39523 ms · 2026-05-18T07:53:15.999573+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Accent Conversion: A Problem-Driven Survey of Sociolinguistic and Technical Constraints

    cs.SD 2026-04 unverdicted novelty 2.0

    The survey reviews the evolution of accent conversion from early DSP approaches to neural models, situating them in linguistic foundations and highlighting constraints, datasets, evaluations, and future directions.

Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages · cited by 1 Pith paper · 2 internal anchors

  1. [1]

    INTRODUCTION Accents, together with timbre and speaking style, form an essen- tial part of speaker identity [1, 2]. However, they can also create challenges in cross-regional communication, and speech technolo- gies trained primarily on standard American or British English of- ten do not transfer well to accented speech [3]. Accent conversion (AC) aims to...

    work page

  2. [2]

    FAC-FACodec: Controllable Zero-Shot Foreign Accent Conversion with Factorized Speech Codec

    BACKGROUND 2.1. Noise-Controlled Translation with Diffusion Priors Diffusion priors enable task-adaptable translation by adding noise to inputs and denoising them toward a learned distribution. SDEdit [9] showed this for images, where partial noising of sketches followed by iterative denoising produced realistic outputs, with the initial noise level actin...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  3. [3]

    Forward Diffusion Process We operate on the FACodec content residualz c1, denotedx 0; its noisy counterpart at timesteptisx t

    PROPOSED FRAMEWORK 3.1. Forward Diffusion Process We operate on the FACodec content residualz c1, denotedx 0; its noisy counterpart at timesteptisx t. Native-accented training pairs are sampled from the empirical joint distribution: (x(n) 0 , π(n))∼p nat(x0, π), n= 1, . . . , N, whereπdenotes aligned phoneme embeddings. With a discrete schedule{β t}T−1 t=...

    work page

  4. [4]

    EXPERIMENTS 4.1. Dataset We train the denoising network on the LJSpeech corpus [13], which contains 13,100 clips (about 24 hours) of a single female speaker with a Standard American English accent reading prepared text. This single-speaker design provides a consistent pronunciation target. We use 11,790 samples for training and 1,310 for validation. Evalu...

    work page 2048

  5. [5]

    Controllability We first evaluate controllability on L2-Arctic across accent–conversion strengthst start (Table 1)

    RESULTS 5.1. Controllability We first evaluate controllability on L2-Arctic across accent–conversion strengthst start (Table 1). Thereconstructioncondition is FACodec encode–decode without conversion; because FACodec learns disen- tangled latents, reconstruction can bias pronunciation toward accents seen in codec training, yielding nonzero accent deltas. ...

    work page

  6. [6]

    CONCLUSION AND FUTURE WORK We presented the first accent conversion framework with an explicit, user-controllable strength parameter. Trained only on native speech with transcripts, our diffusion ODE model operates on FACodec content latents and, at inference, applies controlled noise and it- erative denoising from a chosen start timestept start. This des...

    work page

  7. [7]

    Flexible voices: Identity perception from vari- able vocal signals,

    Nadine Lavan, A. Mike Burton, Sophie K. Scott, and Carolyn McGettigan, “Flexible voices: Identity perception from vari- able vocal signals,”Psychonomic Bulletin & Review, vol. 26, no. 1, pp. 90–102, 2019, Review article

    work page 2019

  8. [8]

    Peter Trudgill, “Accent,” inEncyclopedia of Language and Linguistics, Keith Brown, Ed., p. 14. Elsevier, second edition edition, 2006, [Online]

    work page 2006

  9. [9]

    Tze Tien Ping,Automatic Speech Recognition for Non-Native Speakers, Phd thesis, Universit ´e Joseph-Fourier - Grenoble, 2008

    work page 2008

  10. [10]

    V oice-preserving zero-shot multiple accent conversion,

    Mumin Jin, Prashant Serai, Jilong Wu, Andros Tjandra, Vimal Manohar, and Qing He, “V oice-preserving zero-shot multiple accent conversion,” 2023

    work page 2023

  11. [11]

    Foreign accent conversion in computer assisted pronunciation training,

    Daniel Felps, Heather Bortfeld, and Ricardo Gutierrez-Osuna, “Foreign accent conversion in computer assisted pronunciation training,”Speech Communication, vol. 51, no. 10, pp. 920– 932, 2009, Spoken Language Technology for Education

    work page 2009

  12. [12]

    Accent conversion using discrete units with parallel data syn- thesized from controllable accented tts,

    Tuan Nam Nguyen, Ngoc Quan Pham, and Alexander Waibel, “Accent conversion using discrete units with parallel data syn- thesized from controllable accented tts,” 2024

    work page 2024

  13. [13]

    Accent normalization using self- supervised discrete tokens with non-parallel data,

    Qibing Bai, Sho Inoue, Shuai Wang, Zhongjie Jiang, Yannan Wang, and Haizhou Li, “Accent normalization using self- supervised discrete tokens with non-parallel data,” 2025

    work page 2025

  14. [14]

    Naturalspeech 3: Zero-shot speech synthesis with factorized codec and diffusion models,

    Zeqian Ju, Yuancheng Wang, Kai Shen, Xu Tan, Detai Xin, Dongchao Yang, Yanqing Liu, Yichong Leng, Kaitao Song, Siliang Tang, Zhizheng Wu, Tao Qin, Xiang-Yang Li, Wei Ye, Shikun Zhang, Jiang Bian, Lei He, Jinyu Li, and Sheng Zhao, “Naturalspeech 3: Zero-shot speech synthesis with factorized codec and diffusion models,” 2024

    work page 2024

  15. [15]

    Sdedit: Guided image synthesis and editing with stochastic differential equations,

    Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon, “Sdedit: Guided image synthesis and editing with stochastic differential equations,” 2022

    work page 2022

  16. [16]

    Editts: Score- based editing for controllable text-to-speech,

    Jaesung Tae, Hyeongju Kim, and Taesu Kim, “Editts: Score- based editing for controllable text-to-speech,” 2022

    work page 2022

  17. [17]

    Diffusion-based accent mod- elling in speech synthesis,

    Kamil Deja, Georgi Tinchev, Marta Czarnowska, Marius Cotescu, and Jasha Droppo, “Diffusion-based accent mod- elling in speech synthesis,” inInterspeech 2023, 2023, pp. 5516–5520

    work page 2023

  18. [18]

    Denoising diffusion implicit models,

    Jiaming Song, Chenlin Meng, and Stefano Ermon, “Denoising diffusion implicit models,” 2022

    work page 2022

  19. [19]

    The lj speech dataset,

    Keith Ito and Linda Johnson, “The lj speech dataset,”https: //keithito.com/LJ-Speech-Dataset/, 2017

    work page 2017

  20. [20]

    L2-arctic: A non-native english speech cor- pus,

    Guanlong Zhao, Sinem Sonsaat, Alif Silpachai, Ivana Lu- cic, Evgeny Chukharev-Hudilainen, John Levis, and Ricardo Gutierrez-Osuna, “L2-arctic: A non-native english speech cor- pus,” inProc. Interspeech, 2018, p. 2783–2787

    work page 2018

  21. [21]

    Improving pronunciation and accent con- version through knowledge distillation and synthetic ground- truth from native tts,

    Tuan Nam Nguyen, Seymanur Akti, Ngoc Quan Pham, and Alexander Waibel, “Improving pronunciation and accent con- version through knowledge distillation and synthetic ground- truth from native tts,” 2024

    work page 2024

  22. [22]

    Convert and speak: Zero-shot accent conversion with minimum super- vision,

    Zhijun Jia, Huaying Xue, Xiulian Peng, and Yan Lu, “Convert and speak: Zero-shot accent conversion with minimum super- vision,” inProceedings of the 32nd ACM International Confer- ence on Multimedia. Oct. 2024, MM ’24, p. 4446–4454, ACM

    work page 2024

  23. [23]

    NeMo (Inverse) Text Normalization: From Development to Produc- tion,

    Yang Zhang, Evelina Bakhturina, and Boris Ginsburg, “NeMo (Inverse) Text Normalization: From Development to Produc- tion,” inProc. Interspeech 2021, 2021, pp. 4857–4859

    work page 2021

  24. [24]

    Shal- low Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization,

    Evelina Bakhturina, Yang Zhang, and Boris Ginsburg, “Shal- low Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization,” inProc. Interspeech 2022, 2022

    work page 2022

  25. [25]

    Phonemizer: Text to phones transcription for multiple languages in python,

    Mathieu Bernard and Hadrien Titeux, “Phonemizer: Text to phones transcription for multiple languages in python,”Jour- nal of Open Source Software, vol. 6, no. 68, pp. 3958, 2021

    work page 2021

  26. [26]

    Unsupervised cross- lingual representation learning for speech recognition,

    Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdel- rahman Mohamed, and Michael Auli, “Unsupervised cross- lingual representation learning for speech recognition,”arXiv preprint arXiv:2006.13979, 2020

    work page arXiv 2006

  27. [27]

    Film: Visual reasoning with a general conditioning layer,

    Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron Courville, “Film: Visual reasoning with a general conditioning layer,” 2017

    work page 2017

  28. [28]

    Robust Speech Recognition via Large-Scale Weak Supervision

    Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever, “Robust speech recognition via large-scale weak supervision,”arXiv preprint arXiv:2212.04356, 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  29. [29]

    Commonaccent: Exploring large acoustic pretrained models for accent classification based on common voice,

    Juan Zuluaga-Gomez, Sara Ahmed, Danielius Visockas, and Cem Subakan, “Commonaccent: Exploring large acoustic pretrained models for accent classification based on common voice,”Interspeech 2023, 2023

    work page 2023

  30. [30]

    D.; and Bengio, Y

    Mirco Ravanelli, Titouan Parcollet, Peter Plantinga, Aku Rouhe, Samuele Cornell, Loren Lugosch, Cem Subakan, Nauman Dawalatabad, Abdelwahab Heba, Jianyuan Zhong, Ju-Chieh Chou, Sung-Lin Yeh, Szu-Wei Fu, Chien-Feng Liao, Elena Rastorgueva, Franc ¸ois Grondin, William Aris, Hwidong Na, Yan Gao, Renato De Mori, and Yoshua Ben- gio, “SpeechBrain: A general-pu...

    work page arXiv 2021

  31. [31]

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

    Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shu- jie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yosh- ioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Xiangzhan Yu, and Furu Wei, “Wavlm: Large-scale self-supervised pre-training for full stack speech processing,”IEEE Journal of Selected Topics in Sig...

    work page 2022

  32. [32]

    Method for the sub- jective assessment of intermediate sound quality,

    International Telecommunication Union, “Method for the sub- jective assessment of intermediate sound quality,” 2001

    work page 2001

  33. [33]

    Re- producible subjective evaluation,

    Max Morrison, Brian Tang, Gefei Tan, and Bryan Pardo, “Re- producible subjective evaluation,” inICLR Workshop on ML Evaluation Standards, April 2022

    work page 2022

  34. [34]

    Access: Advancing innovation: NSF’s advanced cyberinfrastructure coordination ecosystem: Services & support,

    Timothy J. Boerner et al., “Access: Advancing innovation: NSF’s advanced cyberinfrastructure coordination ecosystem: Services & support,” inProc. of the Practice and Experience in Advanced Research Computing (PEARC ’23), 2023

    work page 2023