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REVIEW 3 major objections 8 minor 26 references

Source Tracing of Synthetic Speech Systems Through Paralinguistic Pre-Trained Representations

T0 review · 3 major / 8 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that representations from a paralinguistic speech model, TRILLsson, outperform monolingual, multilingual, and speaker-recognition representations for tracing which synthetic speech system generated an audio clip, and that…

desk verdict TRILLsson is a real find for source tracing, but the ASVspoof 2019 SOTA claim rests on a merged-split protocol that risks speaker leakage; the CFAD half of the paper is solid. read the letter →

arxiv 2506.01157 v1 pith:4KOU2YHF submitted 2025-06-01 eess.AS cs.SD

Girish , Mohd Mujtaba Akhtar , Orchid Chetia Phukan , Drishti Singh , Swarup Ranjan Behera , Pailla Balakrishna Reddy , Arun Balaji Buduru , Rajesh Sharma This is my paper · ORCID
classification eess.AScs.SD
keywords sourcetracingsyntheticspeechattributionparalinguisticrepresentationsTRILLssonx-vectorcanonicalcorrelationanalysispre-trainedmodelsASVspoof2019
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

This paper asks whether a speech representation model trained for paralinguistic tasks—pitch, tone, rhythm, emotion—can identify which text-to-speech or voice-conversion system produced a given synthetic audio clip. It argues yes: across a large comparison of pre-trained speech models, TRILLsson, a paralinguistic representation, gives the highest source-attribution accuracy on both the English ASVspoof 2019 data and the Chinese CFAD data. The paper then proposes TRIO, a fusion network that combines two representations with a learned gate, a canonical-correlation alignment loss, and self-attention, and shows that fusing TRILLsson with the speaker-recognition x-vector embedding in TRIO beats every single representation and every concatenation baseline. On the merged ASVspoof 2019 benchmark the best TRIO configuration reaches 99.56 percent accuracy with 0.19 percent equal error rate, and on CFAD 99.04 percent accuracy with 0.95 percent EER, which the authors report as new state of the art for source tracing of synthetic speech generators.

What carries the argument

The central object is TRIO, a fusion framework that takes two frozen speech-representation branches, refines each with convolutional blocks, adaptively weights them through sigmoid gates, aligns them by maximizing a canonical-correlation loss, concatenates them, refines the concatenation with scaled dot-product self-attention, and classifies through a fully connected network. The two representations that carry the result are TRILLsson, a 63-million-parameter distilled universal paralinguistic representation, and x-vector, a speaker-recognition embedding; the paper argues that TRILLsson supplies source-specific prosodic cues while x-vector supplies vocal-tract and speaker-related cues, so their fusion is complementary.

What would settle it

Run the same TRIO (x-vector + TRILLsson) pipeline on ASVspoof 2019 with speaker-disjoint folds; if accuracy falls materially below 99.56 percent, the state-of-the-art claim is partly an artifact of speaker leakage. A second check: replace the gated CCA/self-attention fusion with a simple learned weighted average and the same classifier; if the accuracy gain mostly disappears, the claimed fusion machinery is not doing the work.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that the source of a synthetic voice is best traced through paralinguistic cues, and that TRILLsson—a distilled universal paralinguistic representation—captures those cues better than monolingual, multilingual, and speaker-recognition representations. The paper also discovers that the two best individual representations, TRILLsson and x-vector, are complementary, and that a gated fusion with canonical-correlation alignment and self-attention, the TRIO framework, exploits that complementarity. This combination establishes a new state of the art on both benchmark datasets, improving over the previous best reported results of 98.91 percent accuracy on ASVspoof 2019 and 99.01 percent on CFAD.

Load-bearing premise

The central result depends on the merged ASVspoof 2019 benchmark being a fair test; if random folds reuse the same speakers in training and test, the speaker-identifying parts of the embeddings could inflate the accuracy numbers.

Editorial extensions

If this is right

  • Paralinguistic pre-training is a better prior for synthetic speech source attribution than speaker, multilingual, or monolingual pre-training.
  • Fusing a paralinguistic representation with a speaker-recognition representation via gated CCA and self-attention yields higher attribution accuracy than either representation alone or than plain concatenation.
  • The reported numbers, 99.56 percent accuracy on ASVspoof 2019 and 99.04 percent on CFAD, constitute the new state of the art for source tracing of synthetic speech generation systems on these benchmarks.
  • CNN downstream models outperform FCN models across all tested representations, so the convolutional refinement stage contributes to the result.

Reading between the lines

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

  • If the paralinguistic advantage holds under speaker-disjoint evaluation, forensic source attribution systems should be built around paralinguistic pre-training, and existing detection-only benchmarks should be extended with attribution labels.
  • Because the official ASVspoof 2019 protocol keeps speakers disjoint across partitions, an immediate testable extension is to re-run TRIO with speaker-disjoint folds; that would separate the paralinguistic-signal contribution from possible speaker-identity leakage in speaker-recognition embeddings.
  • The gated CCA plus self-attention fusion recipe could transfer to other attribution tasks, such as identifying which image generator or which language model produced a sample, whenever two complementary representation families exist.
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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 / 8 minor

Summary. The paper investigates source tracing of synthetic speech generation systems (STSGS) by comparing frozen representations from several speech pre-trained models (SPTMs), including the paralinguistic model TRILLsson. The authors report that TRILLsson yields the best single-representation accuracy/EER on ASVspoof 2019 and CFAD, and they propose a fusion framework, TRIO, that combines gated weighting, a canonical-correlation loss, and self-attention. Fusing TRILLsson with x-vector, they report accuracies of 99.56% on ASVspoof 2019 and 99.04% on CFAD, claiming a new state of the art compared with prior work. The paper includes t-SNE visualizations, confusion matrices, and a public code link.

Significance. If the central results hold, the paper makes a useful empirical contribution: it identifies a paralinguistic representation as the strongest single feature for source attribution, demonstrates that combining a paralinguistic model with a speaker-recognition model is beneficial, and provides a reproducible fusion recipe with released code. The CFAD experiments use the official split and support the ranking of representations and the benefit of the selected fusion pair. The main reservation is the ASVspoof 2019 protocol: merging partitions and applying random 5-fold cross-validation can leak speaker identity into training, which is especially relevant for speaker-aware embeddings. Since the abstract and Section IV-B make an unqualified SOTA claim based on those ASV numbers, the core claim requires a protocol-correct rerun.

major comments (3)
  1. [Section IV-A and Tables I-II] The ASVspoof 2019 experiments are run by merging the official train, validation, and test splits and then applying random 5-fold cross-validation. In the official ASVspoof 2019 LA protocol, the training/development partitions and the evaluation partition use disjoint speaker sets, so random folds can place utterances from the same speaker in both training and testing. Because TRILLsson, x-vector, and ECAPA are trained to encode speaker identity, the reported numbers (e.g., 99.56% accuracy and 0.19% EER for TRIO in Table II) and the SOTA comparison in Section IV-B may reflect speaker identification rather than source tracing. Please rerun these experiments under the official evaluation protocol or with speaker-disjoint folds, and report per-fold results. The unqualified SOTA claim should be withheld until protocol-fair numbers are available.
  2. [Section IV-B and Table II] The statement that "fusion of representations through TRIO outperforms the baseline fusion technique" is contradicted by entries in Table II. For example, on ASV, W2V + WP gives concatenation accuracy 96.79% and EER 7.56%, while TRIO gives 95.96% and 7.59%; W2V + XV gives concatenation 96.21% and 7.36%, versus TRIO 95.17% and 7.39%. In both cases, the concatenation baseline is better on both metrics. The claim should be restricted to the selected best pair or supported by a paired significance test over the full set of pairs.
  3. [Section IV-B, SOTA comparison] The best fusion pair (x-vector + TRILLsson) is selected after inspecting the same Table II results on the same ASV folds and CFAD evaluation set that are then used to report final numbers; no held-out model-selection procedure or correction for multiple comparisons is described. The reported gain over prior work [13] is therefore likely optimistic. The authors should specify how the pair was selected, use a validation split for selection, or report the selected model's performance on a fully held-out test set.
minor comments (8)
  1. [Section I] The text refers to "research into SDD" where "SSD" (synthetic speech detection) is intended; please correct the typo.
  2. [Section III-A] The sentence "We perform joint optimization with the cross entropy loss L_CCA" appears to be a typo; the total loss includes both the cross-entropy loss L_CE and the CCA loss, as shown in the displayed equation.
  3. [Section IV-A] "FAD Chinese Dataset" should be "Fake Audio Detection Chinese Dataset" or simply "CFAD," and "chinese" should be capitalized.
  4. [Section IV-A] The hyperparameter lambda is fixed at 0.3 after "preliminary exploration," but no search range or sensitivity analysis is reported; please provide details or a short ablation.
  5. [Section IV-A] The early-stopping criterion is not specified. For ASV, no validation split is described, so it is unclear which data are used to decide when to stop training.
  6. [Section IV-B] The abstract and Section IV-B refer to "baseline fusion methods" in the plural, but only a single concatenation baseline is implemented; either add additional fusion baselines or reword to "concatenation baseline."
  7. [Section II] The sentence "We consider their 300M, 74M, and 1B parameters version" is grammatically awkward and should be rephrased for clarity.
  8. [Section IV-B] Reporting the standard deviation or confidence intervals across the five ASV folds would help assess the stability of the accuracy and EER values in Tables I and II.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the study is an empirical benchmark against public datasets and prior baselines, with no derivation that reduces to its own inputs.

full rationale

The paper's derivation chain is empirical rather than deductive. The central claims are that TRILLsson representations outperform other SPTMs for source tracing of synthetic speech systems, that the proposed TRIO fusion of TRILLsson and x-vector outperforms individual representations and concatenation baselines, and that the resulting scores set a new state of the art relative to Phukan et al. [13]. None of these claims is defined in terms of another, and none is forced by construction. The training objective L = L_CE - lambda * L_CCA is a standard composite loss; lambda = 0.3 is chosen by preliminary exploration, and the reported accuracies and EERs are measured experimental outcomes rather than quantities implied by the loss definition. The comparison with [13] involves overlapping authors, but that work is used only as an external baseline and checkpoint, not as a load-bearing justification for the architecture or as a reason to exclude alternative designs. The CFAD results are obtained on the official evaluation split, which is independent of the reported design choices. The ASVspoof protocol is a validity concern because the train, validation, and test splits are merged before 5-fold cross-validation, which can permit speaker leakage; however, leakage would inflate measured accuracy empirically, not make the result equivalent to the input by definition. The t-SNE plots and confusion matrices are post hoc illustrations rather than derivation steps. I therefore find no circular step in the paper. The score is 0.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central empirical claim rests on standard supervised learning with frozen features and one tuned loss weight. No new physical entities or exotic mathematical axioms are introduced, but the ASVspoof partition-merging assumption is particular to this paper and affects the headline result.

free parameters (1)
  • lambda (CCA loss weight) = 0.3
    Section IV-A says lambda was fixed at 0.3 because preliminary exploration yielded optimal results. The value is tuned on the evaluation datasets and is not swept or cross-validated in the reported experiments.
assumptions (3)
  • domain assumption Mean-pooled last hidden layer of frozen SPTMs preserves enough source-specific information for attribution.
    Section II states representations are extracted from the last hidden state of frozen SPTMs by mean pooling, and all downstream results depend on this extraction choice.
  • ad hoc to paper The official partitions of ASVspoof 2019 can be merged and re-split randomly without introducing speaker leakage.
    Section IV-A says train, validation, and test splits are merged before 5-fold cross-validation. ASVspoof partitions are designed with disjoint speakers, so this assumption is questionable and load-bearing for the ASV results.
  • standard math The CCA loss with inverse square-root covariance matrices is a valid differentiable objective for maximizing inter-representation correlation.
    Section III-A defines LCCA as the trace of the product of inverse square-root covariance matrices. This is a standard CCA-style objective, though the paper does not discuss numerical stability.

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Cite this review

Pith. "Pith review of Source Tracing of Synthetic Speech Systems Through Paralinguistic Pre-Trained Representations." pith.science (2026). https://pith.science/paper/4KOU2YHF

@misc{pith2026250601157,
  author       = {Pith},
  title        = {Pith review of: Source Tracing of Synthetic Speech Systems Through Paralinguistic Pre-Trained Representations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4KOU2YHF}},
  note         = {Machine review of arXiv:2506.01157}
}
read the original abstract

In this work, we focus on source tracing of synthetic speech generation systems (STSGS). Each source embeds distinctive paralinguistic features--such as pitch, tone, rhythm, and intonation--into their synthesized speech, reflecting the underlying design of the generation model. While previous research has explored representations from speech pre-trained models (SPTMs), the use of representations from SPTM pre-trained for paralinguistic speech processing, which excel in paralinguistic tasks like synthetic speech detection, speech emotion recognition has not been investigated for STSGS. We hypothesize that representations from paralinguistic SPTM will be more effective due to its ability to capture source-specific paralinguistic cues attributing to its paralinguistic pre-training. Our comparative study of representations from various SOTA SPTMs, including paralinguistic, monolingual, multilingual, and speaker recognition, validates this hypothesis. Furthermore, we explore fusion of representations and propose TRIO, a novel framework that fuses SPTMs using a gated mechanism for adaptive weighting, followed by canonical correlation loss for inter-representation alignment and self-attention for feature refinement. By fusing TRILLsson (Paralinguistic SPTM) and x-vector (Speaker recognition SPTM), TRIO outperforms individual SPTMs, baseline fusion methods, and sets new SOTA for STSGS in comparison to previous works.

Figures

Figures reproduced from arXiv: 2506.01157 by the authors.

Figure 1
Figure 1. Proposed Framework: TRIO A. TRIO The architecture diagram of the proposed framework, TRIO for fusion of SPTMs representations is shown in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. t-SNE Plots for CFAD [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Confusion Matrix for CFAD using TRIO (x-vector + TRILLsson) ability to capture source-specific paralinguistic cues, which are crucial for distinguishing synthetic speech sources. This validates our hypothesis that paralinguistic SPTM represen￾tations will be the most effective for STSGS attributing to their paralinguistic pre-training. Among all the other SPTMs, speaker recognition SPTMs (x-vector and ECAPA) showed … view at source ↗

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Works this paper leans on

26 extracted references · 19 canonical work pages

  1. [13]

    Investigating prosodic signatures via speech pre-trained models for audio deepfake source attribution,

    O. C. Phukan, D. Singh, S. R. Behera, A. B. Buduru, and R. Sharma, “Investigating prosodic signatures via speech pre-trained models for audio deepfake source attribution,”arXiv preprint arXiv:2412.17796, 2024

    arXiv 2024

  2. [1]

    ASVspoof 2015: The first automatic speaker verification spoofing and countermeasures challenge,

    Z. Wu, T. Kinnunen, N. Evans, J. Yamagishi, C. Hanilc ¸iet al., “ASVspoof 2015: The first automatic speaker verification spoofing and countermeasures challenge,” inProc. of INTERSPEECH, 2015

    work page 2015

  3. [2]

    ASVspoof 2019: Future horizons in spoofed and fake audio detec- tion,

    M. Todisco, X. Wang, V . Vestman, M. Sahidullah, and K. Lee, “ASVspoof 2019: Future horizons in spoofed and fake audio detec- tion,” inProc. of INTERSPEECH, 2019

    work page 2019

  4. [3]

    Aasist: Audio anti-spoofing using integrated spectro-temporal graph attention networks,

    J.-w. Jung, H.-S. Heo, H. Tak, H.-j. Shim, J. S. Chung, B.-J. Lee, H.- J. Yu, and N. Evans, “Aasist: Audio anti-spoofing using integrated spectro-temporal graph attention networks,” inICASSP 2022-2022 IEEE international conference on acoustics, speech and signal pro- cessing (ICASSP). IEEE, 2022, pp. 6367–6371

    2022

  5. [4]

    The vicomtech audio deepfake detection system based on wav2vec2 for the 2022 add challenge,

    J. M. Mart ´ın-Do˜nas and A. ´Alvarez, “The vicomtech audio deepfake detection system based on wav2vec2 for the 2022 add challenge,” inICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 9241– 9245

    work page 2022

  6. [5]

    Improved deepfake detection using whisper features,

    P. Kawa, M. Plata, M. Czuba, P. Szyma ´nski, and P. Syga, “Improved deepfake detection using whisper features,” inInterspeech 2023, 2023, pp. 4009–4013

    work page 2023

  7. [6]

    Audio deep- fake detection with self-supervised wavlm and multi-fusion attentive classifier,

    Y . Guo, H. Huang, X. Chen, H. Zhao, and Y . Wang, “Audio deep- fake detection with self-supervised wavlm and multi-fusion attentive classifier,” inICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024, pp. 12 702–12 706

    work page 2024

  8. [7]

    An initial investigation for detecting vocoder fingerprints of fake audio,

    X. Yan, J. Yi, J. Tao, C. Wang, H. Ma, T. Wang, S. Wang, and R. Fu, “An initial investigation for detecting vocoder fingerprints of fake audio,” inProc. of the 1st International Workshop on Deepfake Detection for Audio Multimedia, 2022

    work page 2022

Show all 26 references
  1. [8]

    Distinguishing neural speech synthesis models through fingerprints in speech waveforms,

    C. Y . Zhang, J. Yi, J. Tao, C. Wang, and X. Yan, “Distinguishing neural speech synthesis models through fingerprints in speech waveforms,”ArXiv, vol. abs/2309.06780, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:261705832

    2023 arXiv

  2. [9]

    Source tracing: Detecting voice spoofing,

    T. Zhu, X. Wang, X. Qin, and M. Li, “Source tracing: Detecting voice spoofing,” inProc. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2022

    2022

  3. [10]

    Source tracing of audio deepfake systems,

    N. Klein, T. Chen, H. Tak, R. Casal, and E. Khoury, “Source tracing of audio deepfake systems,” inInterspeech 2024, 2024, pp. 1100–1104

    2024

  4. [11]

    Audio deepfake attribution: An initial dataset and investigation,

    X. Yan, J. Yi, J. Tao, and J. Chen, “Audio deepfake attribution: An initial dataset and investigation,” 2024. [Online]. Available: https://arxiv.org/abs/2208.10489

    2024 arXiv

  5. [12]

    Attribution of diffusion based deepfake speech generators,

    K. Bhagtani, A. K. S. Yadav, P. Bestagini, and E. J. Delp, “Attribution of diffusion based deepfake speech generators,” in2024 IEEE Interna- tional Workshop on Information Forensics and Security (WIFS), 2024, pp. 1–6

    2024

  6. [14]

    Trillsson: Distilled universal paralinguis- tic speech representations,

    J. Shor and S. Venugopalan, “Trillsson: Distilled universal paralinguis- tic speech representations,” inInterspeech 2022, 2022, pp. 356–360

    2022

  7. [15]

    Heterogeneity over homogeneity: Investigating multilingual speech pre-trained models for detecting audio deepfake,

    O. Chetia Phukan, G. Kashyap, A. B. Buduru, and R. Sharma, “Heterogeneity over homogeneity: Investigating multilingual speech pre-trained models for detecting audio deepfake,” inFindings of the Association for Computational Linguistics: NAACL 2024, K. Duh, H. Gomez, and S. Bet...

    2024

  8. [16]

    Investigation of ensemble of self-supervised models for speech emotion recognition,

    Y . Wu, P. Yue, C. Cheng, and T. Li, “Investigation of ensemble of self-supervised models for speech emotion recognition,” in2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2023, pp. 988–995

    2023

  9. [17]

    wav2vec 2.0: A framework for self-supervised learning of speech representations,

    A. Baevski, Y . Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” Advances in neural information processing systems, vol. 33, pp. 12 449–12 460, 2020

    2020

  10. [18]

    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

  11. [19]

    Unispeech-sat: Universal speech representation learning with speaker aware pre-training,

    S. Chen, Y . Wu, C. Wang, Z. Chen, Z. Chen, S. Liu, J. Wu, Y . Qian, F. Wei, J. Liet al., “Unispeech-sat: Universal speech representation learning with speaker aware pre-training,” inICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICA...

    2022

  12. [20]

    Xls-r: Self-supervised cross-lingual speech representation learning at scale,

    A. Babu, C. Wang, A. Tjandra, K. Lakhotia, Q. Xu, N. Goyal, K. Singh, P. von Platen, Y . Saraf, J. Pino, A. Baevski, A. Conneau, and M. Auli, “Xls-r: Self-supervised cross-lingual speech representation learning at scale,” inInterspeech 2022, 2022, pp. 2278–2282

    2022

  13. [21]

    Robust speech recognition via large-scale weak supervi- sion,

    A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervi- sion,” inInternational conference on machine learning. PMLR, 2023, pp. 28 492–28 518

    2023

  14. [22]

    Scaling speech technology to 1,000+ languages,

    V . Pratap, A. Tjandra, B. Shi, P. Tomasello, A. Babu, S. Kundu, A. Elkahky, Z. Ni, A. Vyas, M. Fazel-Zarandiet al., “Scaling speech technology to 1,000+ languages,”Journal of Machine Learning Re- search, vol. 25, no. 97, pp. 1–52, 2024

    2024

  15. [23]

    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,” in2018 IEEE International Conference on Acoustics, Speech and Signal Pro- cessing (ICASSP), 2018, pp. 5329–5333

    2018

  16. [24]

    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,” 2020

    2020

  17. [25]

    Asvspoof 2019: A large-scale public database of synthesized, converted and replayed speech,

    X. Wang, J. Yamagishi, M. Todisco, H. Delgado, A. Nautsch, N. Evans, M. Sahidullah, V . Vestman, T. Kinnunen, K. A. Leeet al., “Asvspoof 2019: A large-scale public database of synthesized, converted and replayed speech,”Computer Speech & Language, vol. 64, p. 101114, 2020

    2019

  18. [26]

    Cfad: A chinese dataset for fake audio detection,

    H. Ma, J. Yi, C. Wang, X. Yan, J. Tao, T. Wang, S. Wang, and R. Fu, “Cfad: A chinese dataset for fake audio detection,”Speech Communication, vol. 164, p. 103122, 2024

    2024

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