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REVIEW 3 major objections 2 minor 49 references

Task Parameter Extrapolation via Learning Inverse Tasks from Forward Demonstrations

T0 review · 3 major / 2 minor · reviewed 2026-07-15 · grok-4.5

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Pith's one-line read A shared representation of forward and inverse robot tasks lets systems execute inverse skills from forward demonstrations alone in novel configurations, without inverse labels.

desk verdict We only have the abstract for the robotics paper; the supplied full text is a different speech paper, so the central zero-shot inverse claim is still unauditable. read the letter →

arxiv 2603.05576 v3 pith:4EJA4WA4 submitted 2026-03-05 cs.RO

Serdar Bahar , Fatih Dogangun , Matteo Saveriano , Yukie Nagai , Emre Ugur This is my paper · ORCID
classification cs.RO
keywords taskinversionimitationlearningknowledgetransferrobotmanipulationzero-shotgeneralizationskillpoliciesforwarddemonstrations
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Robot skill policies often fail when conditions leave the training region: pure imitation stays local and breaks unpredictably, while many transfer methods need heavy data and still lack accurate zero-shot behavior. This paper targets task inversion—learning how to undo a skill when you mainly saw how to do it. The authors jointly learn a common representation of the forward and inverse tasks, then use auxiliary forward demonstrations from novel configurations so the system can run the matching inverse task with no direct inverse supervision. Ablations plus simulated and real-world manipulation experiments with diverse objects and tools show stronger extrapolation than diffusion-based and multimodal VAE alternatives. A sympathetic reader cares because this promises cheaper, more reliable transfer of reversible skills under changing setups.

What carries the argument

A common shared representation of the forward and inverse tasks, learned jointly so that auxiliary forward demonstrations from novel configurations transfer into inverse execution without inverse labels.

What would settle it

Train with forward demos, add only forward demos from novel object or tool configurations, then score inverse-task success against ground-truth inverse trajectories under matched data budgets; if inverse success collapses or fails to beat the diffusion and multimodal VAE baselines, the central claim fails.

Watch

Extended reading notes

Core claim

The paper claims that a joint learning method which builds a common representation of forward and inverse tasks can, from auxiliary forward demonstrations in novel configurations alone and without any direct inverse supervision, accurately execute the corresponding inverse tasks and extrapolate better than diffusion-based and multimodal VAE baselines on complex simulated and real manipulation.

Load-bearing premise

That a shared forward–inverse representation plus only forward demos from novel configurations is enough to recover accurate inverse execution without any inverse labels.

Editorial extensions

If this is right

  • Inverse skills for new configurations can be recovered using only forward demonstrations, cutting the need for paired inverse labels.
  • Zero-shot inverse execution on reversible manipulation becomes more accurate outside the original training region than with diffusion or multimodal VAE alternatives under the same protocol.
  • Complex multi-object and tool-use skills can be inverted without collecting inverse expert data for every new setup.
  • Ablation-supported joint representation design becomes a practical route to efficient knowledge transfer in task inversion learning.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same shared-representation idea may extend to other dual skill pairs (assemble/disassemble, open/close) beyond strict kinematic inverses.
  • If the common embedding truly encodes invertibility, one could probe it to flag non-invertible skills before execution.
  • Longer-horizon multi-step inversions would test whether the representation stays compositional without inverse supervision.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The submission is presented as arXiv:2603.05576, a robotics paper on task-parameter extrapolation: a joint learning method that builds a shared representation of forward and inverse tasks so that auxiliary forward demonstrations from novel configurations alone enable zero-shot inverse execution without inverse labels, with claimed gains over diffusion and multimodal VAE baselines in simulation and real manipulation. The abstract asserts data-efficient, accurate extrapolation for complex object/tool skills. However, the full manuscript body supplied under that identifier is an unrelated speech paper (DKSD-AE / Koopman-regularized speaker–content disentanglement for speaker verification, consistent with arXiv:2603.05577). No methods, losses, architecture, datasets, ablations, or robot experiments for the robotics claim appear in the provided full text.

Significance. If the robotics claims held as stated—accurate inverse execution from only novel-config forward demos via a shared forward–inverse representation, outperforming diffusion and multimodal VAE alternatives on real complex manipulation—they would be a meaningful contribution to imitation and transfer learning for skill generalization. That significance cannot be assessed from the materials provided: the load-bearing premise (inverse structure recoverable from the common embedding without inverse supervision) is unsupported by any technical content matching the title/abstract. The speech manuscript that is actually present is a separate, potentially interesting SV disentanglement result, but it is not the paper under review.

major comments (3)
  1. Title/abstract vs. full text mismatch: the submission header and abstract describe task inversion / forward–inverse joint learning for robot manipulation (cs.RO, 2603.05576), but the complete manuscript body is DKSD-AE for speaker verification (Koopman multi-step operator + instance normalization, VCTK/TIMIT EER tables). No robotics method, loss, architecture, or experiment is present. The central claim is therefore unauditable; this is a load-bearing integrity issue for any evaluation of 2603.05576.
  2. Load-bearing premise of the claimed paper (abstract): that a common forward–inverse representation plus only auxiliary forward demos at novel configurations suffices for accurate zero-shot inverse execution without inverse labels. With only the abstract available for that claim, there is no equation, training objective, architecture, or ablation that could support or refute recoverability of inverse structure. Soundness of the robotics contribution cannot be established from the supplied full text.
  3. Empirical claims in the abstract (outperformance of diffusion-based and multimodal VAE alternatives; ablations; sim and real complex manipulation with diverse objects/tools) have no corresponding tables, protocols, metrics, or error bars in the provided manuscript. Comparison baselines and success criteria for the robotics setting are undefined in the full text.
minor comments (2)
  1. If the intended submission was the speech paper present in the body, it should be resubmitted under its correct title, arXiv id, and venue scope (cs.SD / speaker verification), not under the robotics abstract.
  2. Abstract of the robotics claim uses strong language (“successfully execute,” “outperform”) without any quantitative summary that could be checked even at abstract level (e.g., success rates, number of objects/tools, number of seeds).

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found: robotics claim is empirical and unauditable from the supplied text; the provided full manuscript is an unrelated speech paper that is also non-circular.

full rationale

The target paper (arXiv 2603.05576) asserts an empirical result: a joint forward–inverse representation plus auxiliary forward demonstrations from novel configurations enables zero-shot inverse execution without inverse labels, outperforming diffusion and multimodal VAE baselines. That claim is not a first-principles derivation and does not, on the abstract alone, reduce any reported quantity to a fitted input by construction. The CACHEABLE full manuscript, however, is a different paper (DKSD-AE speaker verification / arXiv 2603.05577): multi-step Koopman operator learning plus instance normalization, trained with reconstruction / multi-step prediction / eigenvalue losses and evaluated by speaker and content EER on VCTK and TIMIT. Its inductive biases are external (Koopman theory, Chou-style instance norm, SKD eigenvalue penalty from Berman et al.), its ablations compare multi-step vs single-step vs reconstruction-only, and its metrics are held-out verification EERs—not self-fulfilling fits renamed as predictions. No self-definitional loop, no fitted-input-called-prediction, no load-bearing self-citation uniqueness theorem, and no renaming of a known result as a derivation appear in either the robotics abstract or the supplied speech manuscript. Circularity score is therefore 0; residual risk is content mismatch / unauditability of the robotics methods, not circular construction.

Assumptions & free parameters 2 free parameters · 3 assumptions · 1 invented entities

Abstract-only review of the robot paper; free parameters and invented modules cannot be enumerated from equations. Domain assumptions are those required for the central transfer claim. No formal axioms or new physical entities appear in the abstract.

free parameters (2)
  • Unspecified model/training hyperparameters of the joint forward–inverse learner
    Any shared representation and policy/trajectory model will have architecture sizes, loss weights, and training schedules; none are given in the abstract but the empirical claim depends on them.
  • Amount and distribution of auxiliary forward demonstrations at novel configurations
    The method’s zero-shot inverse claim still relies on collecting forward demos in new setups; how many and how they cover the novel region is unspecified and load-bearing for reported accuracy.
assumptions (3)
  • domain assumption Forward and inverse tasks of the same skill share structure that can be captured in one common representation usable for inverse execution.
    Core premise of the joint learning approach stated in the abstract; if false for the target skills, auxiliary forward demos would not unlock inverse policies.
  • ad hoc to paper Auxiliary forward demonstrations from novel configurations, without inverse labels, suffice for accurate inverse task execution in those configurations.
    This is the paper’s distinctive operational assumption for ‘without any direct supervision’ inverse transfer.
  • domain assumption Imitation policies fail outside the training region while the proposed transfer remains accurate in zero-shot inverse settings.
    Problem framing in the abstract that motivates the method and the comparison to IL and transfer baselines.
invented entities (1)
  • Common forward–inverse task representation (joint learning framework for task inversion)
    purpose: Map forward demos and inverse execution into one space so novel forward demos enable inverse skills without inverse supervision.
    Named as the method’s core construct in the abstract; no independent external evidence or formal definition available in the provided materials.

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Cite this review

Pith. "Pith review of Task Parameter Extrapolation via Learning Inverse Tasks from Forward Demonstrations." pith.science (2026). https://pith.science/paper/4EJA4WA4

@misc{pith2026260305576,
  author       = {Pith},
  title        = {Pith review of: Task Parameter Extrapolation via Learning Inverse Tasks from Forward Demonstrations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4EJA4WA4}},
  note         = {Machine review of arXiv:2603.05576}
}
read the original abstract

Generalizing skill policies to novel conditions remains a key challenge in robot learning. Imitation learning methods, while data-efficient, are largely confined to the training region and consistently fail on input data outside it, leading to unpredictable policy failures. Alternatively, transfer learning approaches offer methods for trajectory generation robust to both changes in environment and tasks, but they remain data-hungry and lack accuracy in zero-shot generalization. We address these challenges in the context of task inversion learning and propose a novel joint learning approach to achieve accurate and efficient knowledge transfer. Our method constructs a common representation of the forward and inverse tasks, and leverages auxiliary forward demonstrations from novel configurations to successfully execute the corresponding inverse tasks, without any direct supervision. We demonstrate the extrapolation capabilities of our framework through ablation studies and experiments in simulated and real-world environments that require complex manipulation skills with a diverse set of objects and tools, where we outperform diffusion-based and multimodal VAE alternatives.

Discussion (0). Continue with ORCID to comment.

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Works this paper leans on

49 extracted references · 2 linked inside Pith

  1. [1]

    X-Vectors: Robust DNN Embeddings for Speaker Recognition,

    D. Snyder, D. Garcia-Romero, G. Sell, D. Povey, and S. Khudanpur, “X-Vectors: Robust DNN Embeddings for Speaker Recognition,” in ICASSP, 2018, pp. 5329–5333

    2018

  2. [2]

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

    B. Desplanques, J. Thienpondt, and K. Demuynck, “Ecapa-tdnn: Em- phasized channel attention, propagation and aggregation in tdnn based speaker verification,” inInterspeech, 2020, pp. 3830–3834

    2020

  3. [3]

    Hubert: Self-supervised speech representation learning by masked prediction of hidden units,

    W.-N. Hsu, B. Bolte, Y .-H. H. Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed, “Hubert: Self-supervised speech representation learning by masked prediction of hidden units,”IEEE/ACM Trans. Audio, Speech, Language Process., vol. 29, pp. 3451–3460, 2021

    2021

  4. [4]

    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. Xiao, J. Wu, L. Zhou, S. Ren, Y . Qian, Y . Qian, J. Wu, M. Zeng, X. Yu, and F. Wei, “WavLM: Large-Scale Self-Supervised Pre- Training for Full Stack Speech Processing,”IEEE J. Sel. Topics Signal Process., vol. 16, no. 6, pp. 1505–1518, Oct. 2022

    2022

  5. [5]

    Unsupervised TTS Acoustic Modeling for TTS With Conditional Disentangled Sequential V AE,

    J. Lian, C. Zhang, G. K. Anumanchipalli, and D. Yu, “Unsupervised TTS Acoustic Modeling for TTS With Conditional Disentangled Sequential V AE,”IEEE/ACM Trans. Audio, Speech, Language Process., vol. 31, pp. 2548–2557, 2023

    2023

  6. [6]

    Disentangling Speech Representations Learning With Latent Diffusion for Speaker Verification,

    Z. Li, M.-W. Mak, J.-T. Chien, M. Pilanci, Z. Jin, and H. Meng, “Disentangling Speech Representations Learning With Latent Diffusion for Speaker Verification,”IEEE/ACM Trans. Audio, Speech, Language Process., vol. 33, pp. 3896–3907, 2025

    2025

  7. [7]

    Rose,Forensic Speaker Identification

    P. Rose,Forensic Speaker Identification. London: CRC Press, Jul. 2002

    2002

  8. [8]

    Disentangled Representation Learning,

    X. Wang, H. Chen, S. Tang, Z. Wu, and W. Zhu, “Disentangled Representation Learning,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 46, no. 12, pp. 9677–9696, Dec. 2024

    2024

Show all 49 references
  1. [9]

    Auto-encoding variational bayes,

    D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in Proc. of the Int. Conf. on Learn. Representations, 2014

    2014

  2. [10]

    Challenging common assumptions in the unsupervised learning of disentangled representations,

    F. Locatello, S. Bauer, M. Lucic, G. Raetsch, S. Gelly, B. Sch ¨olkopf, and O. Bachem, “Challenging common assumptions in the unsupervised learning of disentangled representations,” inProc. of the Int. Conf. on Mach. Learn., 2019, pp. 4114–4124

    2019

  3. [11]

    Neural discrete representation learning,

    A. van den Oord, O. Vinyals, and K. Kavukcuoglu, “Neural discrete representation learning,” inProc. of the Int. Conf. on Neural Inf. Process. Syst., 2017, p. 6309–6318

    2017

  4. [12]

    Unsupervised learning of disentan- gled and interpretable representations from sequential data,

    W.-N. Hsu, Y . Zhang, and J. Glass, “Unsupervised learning of disentan- gled and interpretable representations from sequential data,” inProc. of the Int. Conf. on Neural Inf. Proc. Syst., 2017, pp. 1876–1887

    2017

  5. [13]

    Disentangled Sequential Autoencoder,

    L. Yingzhen and S. Mandt, “Disentangled Sequential Autoencoder,” in Proc. of the Int. Conf. on Mach. Learn., vol. 80, 2018, pp. 5670–5679

    2018

  6. [14]

    Hamiltonian Systems and Transformation in Hilbert Space,

    B. O. Koopman, “Hamiltonian Systems and Transformation in Hilbert Space,”Proc. of the National Academy of Sciences, vol. 17, no. 5, pp. 315–318, May 1931

    1931

  7. [15]

    Spectral analysis of nonlinear flows,

    C. W. Rowley, I. Mezic, S. Bagheri, P. Schlatter, and D. S. Henningson, “Spectral analysis of nonlinear flows,”J. of Fluid Mechanics, vol. 641, pp. 115–127, 2009

    2009

  8. [16]

    Instance Normalization: The Missing Ingredient for Fast Stylization,

    D. Ulyanov, A. Vedaldi, and V . Lempitsky, “Instance Normalization: The Missing Ingredient for Fast Stylization,” 2017, arXiv:1607.08022

    2017 arXiv

  9. [17]

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

    J. C. Chou and H. Y . Lee, “One-shot voice conversion by separating speaker and content representations with instance normalization,” in Interspeech, 2019, pp. 664–668

    2019

  10. [18]

    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,” inICASSP, 2021, pp. 5954–5958

    2021

  11. [19]

    Unsupervised Representation Disentanglement Using Cross Domain Features and Adversarial Learning in Variational Au- toencoder Based V oice Conversion,

    W.-C. Huang, H. Luo, H.-T. Hwang, C.-C. Lo, Y .-H. Peng, Y . Tsao, and H.-M. Wang, “Unsupervised Representation Disentanglement Using Cross Domain Features and Adversarial Learning in Variational Au- toencoder Based V oice Conversion,”IEEE Trans. on Emerg. Topics in Comput. In...

    2020

  12. [20]

    SpeechTripleNet: End-to-End Disentangled Speech Representation Learning for Content, Timbre and Prosody,

    H. Lu, X. Wu, Z. Wu, and H. Meng, “SpeechTripleNet: End-to-End Disentangled Speech Representation Learning for Content, Timbre and Prosody,” inProc. of the ACM Int. Conf. on Multimedia, 2023, pp. 2829–2837

    2023

  13. [21]

    Disentangling prosody and timbre embeddings via voice conversion,

    N. Gengembre, O. Le Blouch, and C. Gendrot, “Disentangling prosody and timbre embeddings via voice conversion,” inInterspeech, 2024, pp. 2765–2769

    2024

  14. [22]

    Robust Disentangled Variational Speech Representation Learning for Zero-Shot V oice Conversion,

    J. Lian, C. Zhang, and D. Yu, “Robust Disentangled Variational Speech Representation Learning for Zero-Shot V oice Conversion,” inICASSP, 2022, pp. 6572–6576

    2022

  15. [23]

    Contrastively Disentangled Sequential Variational Autoencoder,

    J. Bai, W. Wang, and C. Gomes, “Contrastively Disentangled Sequential Variational Autoencoder,” inAdv. in Neural Inf. Proc. Syst., 2021, pp. 10 105–10 118. 10

    2021

  16. [24]

    Unifying One-Shot V oice Conversion and Cloning with Disentangled Speech Representations,

    H. Lu, X. Wu, H. Guo, S. Liu, Z. Wu, and H. Meng, “Unifying One-Shot V oice Conversion and Cloning with Disentangled Speech Representations,” inICASSP, 2024, pp. 11 141–11 145

    2024

  17. [25]

    Dynamic mode decomposition of numerical and experi- mental data,

    P. J. Schmid, “Dynamic mode decomposition of numerical and experi- mental data,”J. of Fluid Mechanics, vol. 656, pp. 5–28, 2010

    2010

  18. [26]

    A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition,

    M. O. Williams, I. G. Kevrekidis, and C. W. Rowley, “A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition,”J. of Nonlinear Sci., vol. 25, no. 6, pp. 1307–1346, 12 2015

    2015

  19. [27]

    Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition,

    S. T. M. Dawson, M. S. Hemati, M. O. Williams, and C. W. Rowley, “Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition,”Experiments in Fluids, vol. 57, no. 3, p. 42, Feb. 2016

    2016

  20. [28]

    Modern koopman theory for dynamical systems,

    S. L. Brunton, M. Budi ˇsi´c, E. Kaiser, and J. N. Kutz, “Modern koopman theory for dynamical systems,”SIAM Review, vol. 64, no. 2, pp. 229– 340, 2022

    2022

  21. [29]

    Learning Koopman invariant subspaces for dynamic mode decomposition,

    N. Takeishi, Y . Kawahara, and T. Yairi, “Learning Koopman invariant subspaces for dynamic mode decomposition,” inAdv. in Neural Inf. Process. Syst., 2017, pp. 1131–1141

    2017

  22. [30]

    Deep learning for universal linear embeddings of nonlinear dynamics,

    B. Lusch, J. N. Kutz, and S. L. Brunton, “Deep learning for universal linear embeddings of nonlinear dynamics,”Nature Communications, vol. 9, no. 1, pp. 1–10, 11 2018

    2018

  23. [31]

    Forecasting sequential data using consistent koopman autoencoders,

    O. Azencot, N. B. Erichson, V . Lin, and M. Mahoney, “Forecasting sequential data using consistent koopman autoencoders,” inProc. of the Int. Conf. on Machine Learning, 2020, pp. 475–485

    2020

  24. [32]

    Transformers for Modeling Physical Sys- tems,

    N. Geneva and N. Zabaras, “Transformers for Modeling Physical Sys- tems,”Neural Networks, vol. 146, pp. 272–289, Feb. 2022

    2022

  25. [33]

    Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors,

    Y . Liu, C. Li, J. Wang, and M. Long, “Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors,” inAdv. in Neural Inf. Process. Syst., 2023, pp. 12 271–12 290

    2023

  26. [34]

    Multifactor Sequential Disen- tanglement via Structured Koopman Autoencoders,

    N. Berman, I. Naiman, and O. Azencot, “Multifactor Sequential Disen- tanglement via Structured Koopman Autoencoders,” inProc. of the Int. Conf. on Learn. Representations, 2023, pp. 1–25

    2023

  27. [35]

    Hierarchical Koopman Diffusion: Fast Generation with Interpretable Diffusion Trajectory,

    H. Bai, W. Ding, and D. Zou, “Hierarchical Koopman Diffusion: Fast Generation with Interpretable Diffusion Trajectory,” inAdv. in Neural Inf. Process. Syst., Oct. 2025

    2025

  28. [36]

    Koopman Neural Forecaster for Time Series With Temporal Distribution Shifts,

    R. Wang, Y . Dong, S. Arik, and R. Yu, “Koopman Neural Forecaster for Time Series With Temporal Distribution Shifts,” inProc. of the Int. Conf. on Learn. Representations, 2023

    2023

  29. [37]

    Linearly recurrent autoencoder networks for learning dynamics,

    S. E. Otto and C. W. Rowley, “Linearly recurrent autoencoder networks for learning dynamics,”SIAM J. on Appl. Dynamical Syst., vol. 18, no. 1, pp. 558–593, Mar. 2019

    2019

  30. [38]

    Compressed dynamic mode decomposition for background modeling,

    N. B. Erichson, S. L. Brunton, and J. N. Kutz, “Compressed dynamic mode decomposition for background modeling,”J. of Real-Time Image Proc., vol. 16, no. 5, pp. 1479–1492, Oct. 2019

    2019

  31. [39]

    Long Short-Term Memory,

    S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,”Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997

    1997

  32. [40]

    Deep Residual Learning for Image Recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” inProc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016, pp. 770–778

    2016

  33. [41]

    Deep Speaker: an End-to-End Neural Speaker Embedding System,

    C. Li, X. Ma, B. Jiang, X. Li, X. Zhang, X. Liu, Y . Cao, A. Kannan, and Z. Zhu, “Deep Speaker: an End-to-End Neural Speaker Embedding System,” 2017, arXiv:1705.02304

    2017 arXiv

  34. [42]

    Instance-based temporal normalization for speaker Verification,

    T. Lertpetchpun and E. Chuangsuwanich, “Instance-based temporal normalization for speaker Verification,” inInterspeech, 2023, pp. 3172– 3176

    2023

  35. [43]

    SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition,

    D. S. Park, W. Chan, Y . Zhang, C.-C. Chiu, B. Zoph, E. D. Cubuk, and Q. V . Le, “SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition,” inInterspeech, 2019, pp. 2613–2617

    2019

  36. [44]

    Decoupled Weight Decay Regularization,

    I. Loshchilov and F. Hutter, “Decoupled Weight Decay Regularization,” inProc. of the Int. Conf. on Learn. Representations, 2017

    2017

  37. [45]

    CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR V oice Cloning Toolkit (version 0.92),

    J. Yamagishi, C. Veaux, and K. MacDonald, “CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR V oice Cloning Toolkit (version 0.92),” Nov. 2019

    2019

  38. [46]

    TIMIT Acoustic-Phonetic Continuous Speech Corpus,

    Garofolo, John S., Lamel, Lori F., Fisher, William M., Pallett, David S., Dahlgren, Nancy L., Zue, Victor, and Fiscus, Jonathan G., “TIMIT Acoustic-Phonetic Continuous Speech Corpus,” 1993

    1993

  39. [47]

    WebRTC voice activity detection,

    Google Inc., “WebRTC voice activity detection,” https://webrtc.org/, accessed: Jan. 2026

    2026

  40. [48]

    Visualizing Data using t-SNE,

    L. v. d. Maaten and G. Hinton, “Visualizing Data using t-SNE,”J. of Mach. Learn. Res., vol. 9, no. 86, pp. 2579–2605, 2008

    2008

  41. [49]

    Transformers in speech processing: Over- coming challenges and paving the future,

    S. Latif, S. A. M. Zaidi, H. Cuay ´ahuitl, F. Shamshad, M. Shoukat, M. Usama, and J. Qadir, “Transformers in speech processing: Over- coming challenges and paving the future,”Comput. Sci. Rev., vol. 58, p. 100768, Nov. 2025

    2025

Pith tools

Reviewed July 15, 2026 · model on record in the stance chip above.