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REVIEW 4 major objections 6 minor 35 references

BanglaFake: Constructing and Evaluating a Specialized Bengali Deepfake Audio Dataset

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper introduces BanglaFake, which it presents as the first publicly available Bengali deepfake audio dataset, with 12,260 real and 13,260 synthetic utterances, and argues it can serve as a benchmark for detector training and…

desk verdict A genuinely novel resource undermined by a speaker-identity confound and inconsistent numbers; the dataset idea is worth something, but the paper as written does not support a deepfake benchmark. read the letter →

arxiv 2505.10885 v1 pith:DMJ6JMN5 submitted 2025-05-16 cs.SD cs.AIcs.MMeess.AS

Istiaq Ahmed Fahad , Kamruzzaman Asif , Sifat Sikder This is my paper · ORCID
classification cs.SDcs.AIcs.MMeess.AS
keywords Bengalideepfakeaudiodetectiontext-to-speechVITSlow-resourcelanguagespeakerverificationspeechforensicsmeanopinionscore
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 introduces BanglaFake, a new audio dataset meant to close a gap: Bengali has almost no public resources for training and testing deepfake-audio detectors, even though voice spoofing is a known risk to speaker-verification systems. The dataset contains 12,260 real Bengali utterances and 13,260 synthetic utterances generated by a VITS-based end-to-end text-to-speech model trained from scratch on a phonetically balanced Bengali speech corpus. The authors evaluate the fake audio with 30 native Bengali listeners, reporting a Robust-MOS of 3.40 for naturalness and 4.01 for intelligibility, and use t-SNE on MFCC features to show that real and fake samples overlap substantially. If the dataset holds up as described, it would give low-resource Bengali speech forensics a common benchmark and a training resource that did not previously exist.

What carries the argument

The load-bearing component is VITS, a named end-to-end text-to-speech architecture that combines a conditional variational autoencoder with adversarial training: a posterior encoder extracts latent variables from a target spectrogram, a normalizing flow and stochastic duration predictor model phoneme timing, and an adversarial neural vocoder decoder converts the latent variables into raw waveforms. The authors train VITS on the phonetically balanced Bengali corpus, then use it to synthesize the fake half of the dataset. The dataset's standardized text-audio metadata layout makes the pairs directly usable by common TTS and detection pipelines, and the evaluation rests on MOS listening tests and t-SNE visualization of MFCCs.

What would settle it

Run a speaker-embedding comparison on the additional corpus pairs: if the cosine similarity between a fake clip and its paired real clip is much lower than the similarity between the fake clip and a reference clip from the main corpus's speaker, the fake class is carrying a different speaker's identity, and the benchmark is testing speaker discrimination rather than deepfake detection. A listener study asking whether each fake clip sounds like the person in the paired real clip would also settle it.

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Extended reading notes

Core claim

The paper's central claim is that BanglaFake is the first publicly available Bengali deepfake audio dataset and a usable benchmark for detecting synthetic speech in Bengali. It pairs real recordings from two sources—a phonetically balanced Bengali speech corpus and a crowd-sourced multilingual corpus—with deepfake clips generated from the same text by the authors' VITS text-to-speech model. The authors report that human listeners rate the synthetic speech as reasonably natural (Robust-MOS 3.40) and highly intelligible (4.01), and that t-SNE projections of MFCC features place many real and fake samples in overlapping regions, which they interpret as evidence that the fakes are hard to distinguish from real speech.

Load-bearing premise

The benchmark's validity rests on the assumption that the fake clips are deepfakes of the paired real clips' speakers, but the model was trained on a single voice from the main corpus, so the additional corpus pairs may present a different speaker as fake.

Editorial extensions

If this is right

  • A public Bengali real/fake corpus makes it possible to train supervised deepfake detectors where none could be trained before in this language.
  • The dataset gives speaker-verification researchers a low-resource test set for spoofing countermeasures, extending standard countermeasure research to Bengali.
  • The reported MOS scores suggest synthetic clips are natural enough that simple energy- or artifact-based detectors will not trivially separate the classes.
  • The overlap seen in t-SNE motivates detectors that use learned representations rather than raw MFCCs.
  • The dataset structure supports cross-lingual comparison and zero-shot detection experiments, since the metadata and format match existing TTS pipelines.

Reading between the lines

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

  • Because the text-to-speech model was trained on a single male voice from the main corpus, the fake clips paired with the other corpus's speakers are likely spoken in that male voice rather than in the paired speaker's voice; if so, a detector trained on these pairs could succeed by spotting speaker mismatch, not synthesis artifacts.
  • The same confound means the t-SNE overlap should not be read solely as high naturalness; it may partly reflect domain or speaker differences between the two real-speech sources and the synthetic clips.
  • A stronger test of the benchmark would add matched-speaker synthesis (same target voice for real and fake) or voice conversion, and report detection accuracy separately for the matched and mismatched subsets.
  • A quick validation experiment would be to ask listeners or a speaker-embedding model whether each fake clip sounds like the paired real clip's speaker; if it does not, the dataset documents voice replacement rather than speaker-preserving deepfake.
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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

4 major / 6 minor

Summary. The paper introduces BanglaFake, a Bengali audio dataset claimed to contain 12,260 real and 13,260 synthetic utterances, with real speech from the SUST TTS corpus and Mozilla Common Voice and synthetic speech generated by a VITS model trained on the SUST corpus. The authors report a mean opinion score evaluation with 30 native Bengali listeners and a t-SNE visualization of MFCC features, and they claim that the dataset is the first public Bengali deepfake audio benchmark.

Significance. If the dataset were constructed as claimed, it would fill a real gap in low-resource audio forensics, and the public release of the dataset and code is a positive feature. However, the current manuscript does not establish that the fake clips are deepfakes of the paired real clips for the Common Voice subset, and the internal dataset statistics are inconsistent. The significance is therefore conditional on a substantial reconstruction of the dataset and its evaluation.

major comments (4)
  1. [§4.2, §3.1, Table 1, §6] The Common Voice portion of the dataset does not implement a deepfake task. The VITS model was trained only on the SUST corpus (Section 4.1) and is a single-speaker male model (Table 1 and Section 6), yet Section 4.2 states that the fake audio paired with Common Voice real audio was 'generated using our model.' Consequently, for every Common Voice pair, the real clip is from one of five Common Voice speakers while the fake clip is the SUST male voice. A detector can separate these pairs by speaker identity alone, for example with any off-the-shelf speaker embedding, without detecting synthesis artifacts. This affects the 2,260 (or 2,797, see next comment) Common Voice samples and invalidates the benchmark claim for that subset.
  2. [Table 2; Abstract; Table 1; §4.2] The reported dataset sizes are internally inconsistent. Table 2's rows sum to 12,797 real and 12,797 fake, but the row labeled 'Total' says 13,260; the abstract and Table 1 say 12,260 real and 13,260 fake; and Section 4.2 says 2,260 Common Voice samples while the five Common Voice rows in Table 2 sum to 2,797. The released dataset cannot be checked or reproduced against these numbers, and the discrepancy affects the central quantitative claims of the paper.
  3. [§5.2] The only quantitative evaluation is a t-SNE plot of MFCCs for 1,000 randomly selected pairs. t-SNE overlap is not a detection metric: it depends on hyperparameters and initialization, and it cannot be used to estimate error rates. No deepfake detector baseline (for example, wav2vec 2.0 or LFCC-LCNN with an EER) is reported, so the paper provides no evidence that the dataset is usable as a benchmark. Moreover, if the Common Voice fakes come from a different speaker, the t-SNE plot is not even informative about synthesis artifacts.
  4. [§5.1] The MOS evaluation rates only the synthesized audio and does not compare it against the corresponding real audio, nor does it report inter-rater variability, so it does not by itself support the claim that real and fake audio are hard to distinguish. The text is also internally inconsistent: it says 10 sentences were selected for evaluation, but then states that participants listened to five sets of clips.
minor comments (6)
  1. [Abstract and Introduction] The dataset name is spelled 'BangalFake' in the abstract and introduction, while the title and the rest of the paper use 'BanglaFake'; this should be made consistent.
  2. [§4.2] In the sentence 'Additionally, we include2,260 text and real audio samples', a space is missing between 'include' and '2,260'; the manuscript needs a careful proofreading pass.
  3. [References] Reference [16] and reference [25] are the same Common Voice paper, and reference [17] and reference [19] are the same VITS paper; these duplicates should be consolidated.
  4. [Figure 2] The t-SNE visualization lacks the hyperparameters used (perplexity, number of iterations, random state) and the MFCC configuration, so the figure is not reproducible.
  5. [§3.5] The dataset release description does not provide file counts, exact license terms, total duration, or a train/validation/test split, which makes it difficult for readers to verify the 'publicly available' claim.
  6. [§6] The conclusion says the deepfake audio was generated for a 'single speaker (male)', while Table 1 reports seven real speakers; the relationship between these numbers should be clarified, especially after the Common Voice discussion.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the dataset construction and evaluation are self-contained; the speaker-mismatch concern is a validity issue, not a circular reduction.

full rationale

BanglaFake is a dataset-construction paper rather than a derivation paper. It trains a VITS model on the SUST TTS corpus (Section 4.1), generates synthetic audio (Section 4.2), and evaluates it with human MOS ratings and t-SNE visualization (Section 5). None of these steps involves fitting a parameter to a target quantity and then presenting that quantity as an independent prediction; nor does the paper invoke a uniqueness theorem or load-bearing self-citation. The MOS evaluation is a standard TTS quality assessment of the generated audio, not a derived detection result. The t-SNE plot is qualitative evidence of overlap, not a fitted-prediction reduction. The strongest skeptical concern, that Common Voice fake clips use a single SUST male speaker rather than the paired real speaker, is a potential threat to the dataset's validity as a deepfake benchmark, but it is not circularity: the paper does not define its benchmark outcome in terms of the same data used to produce it. The paper's central claim is the availability and composition of a new dataset, and that claim is not equivalent by construction to any fitted input. Therefore the circularity score is 0, with no specific circular steps identified.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the TTS model being a realistic deepfake generator, on the source corpora being correctly labeled real speech, on the pairing of real and fake clips producing a meaningful detection task, and on t-SNE overlap being interpreted as difficulty. None of these are independently verified in the paper.

assumptions (4)
  • domain assumption VITS trained on SUST produces audio that is representative of Bengali deepfakes.
    All fake audio in the dataset depends on this model; the paper provides only a small MOS study and no external detection validation.
  • domain assumption The SUST TTS Corpus is phonetically balanced and a valid source of real Bengali speech.
    SUST is an existing corpus; the paper relies on it for both real audio and TTS training.
  • ad hoc to paper For the Common Voice subset, pairing text-matched real and fake clips yields a meaningful deepfake detection task even though the speakers differ.
    The fake clips do not mimic the paired real speaker's voice, so this assumption is questionable.
  • ad hoc to paper t-SNE overlap between real and fake MFCC features is evidence of detection difficulty.
    t-SNE is a visualization; without a detector baseline, overlap does not quantify separability.

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Cite this review

Pith. "Pith review of BanglaFake: Constructing and Evaluating a Specialized Bengali Deepfake Audio Dataset." pith.science (2026). https://pith.science/paper/DMJ6JMN5

@misc{pith2026250510885,
  author       = {Pith},
  title        = {Pith review of: BanglaFake: Constructing and Evaluating a Specialized Bengali Deepfake Audio Dataset},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DMJ6JMN5}},
  note         = {Machine review of arXiv:2505.10885}
}
read the original abstract

Deepfake audio detection is challenging for low-resource languages like Bengali due to limited datasets and subtle acoustic features. To address this, we introduce BangalFake, a Bengali Deepfake Audio Dataset with 12,260 real and 13,260 deepfake utterances. Synthetic speech is generated using SOTA Text-to-Speech (TTS) models, ensuring high naturalness and quality. We evaluate the dataset through both qualitative and quantitative analyses. Mean Opinion Score (MOS) from 30 native speakers shows Robust-MOS of 3.40 (naturalness) and 4.01 (intelligibility). t-SNE visualization of MFCCs highlights real vs. fake differentiation challenges. This dataset serves as a crucial resource for advancing deepfake detection in Bengali, addressing the limitations of low-resource language research.

Figures

Figures reproduced from arXiv: 2505.10885 by the authors.

Figure 1
Figure 1. Overview of the Bangla Fake Speech Dataset Generation Pipeline. The left side illustrates the process of extracting latent speech representations from the SUST TTS corpus using a posterior encoder, normalizing flow, and stochastic duration predictor. The right side depicts the synthesis process, where text from the Bangla dataset is encoded and converted into speech-like representations using a trained model. The fi… view at source ↗
Figure 2
Figure 2. t-SNE visualization of MFCC features for real and deepfake audio samples. Real audio samples are represented in blue circles, while deepfake audio samples are shown in red squares. The overlap between the two classes highlights the challenge of distinguishing fake audio from real audio. 5.1. Qualitative Evaluation We conducted a qualitative evaluation to assess the realism and perceptual quality of our dataset. For … view at source ↗

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Reference graph

Works this paper leans on

35 extracted references · 20 canonical work pages

  1. [1]

    Auto- matic speaker verification (ASV) tools, widely used for bio- metric identification, are increasingly vulnerable to spoofing at- tacks, particularly deepfake audios

    Introduction Deepfake audio generation presents a growing challenge for speech processing and biometric verification systems. Auto- matic speaker verification (ASV) tools, widely used for bio- metric identification, are increasingly vulnerable to spoofing at- tacks, particularly deepfake audios. Since the term ‘deepfake’ was coined in 2017, AI-driven mani...

    work page 2017

  2. [2]

    BanglaFake: Constructing and Evaluating a Specialized Bengali Deepfake Audio Dataset

    Related Work 2.1. Deepfake Detection Models Audio deepfake detection has advanced significantly, leverag- ing machine learning techniques to distinguish between real and synthetic speech. Traditional methods rely on handcrafted feature extraction, such as Mel-Frequency Cepstral Coefficients (MFCCs) and spectrogram-based analysis, combined with clas- sifie...

    work page Pith review arXiv 2025

  3. [3]

    Dataset Description The BanglaFake dataset is designed to support developing and evaluating deepfake audio detection models specifically for the Bengali language. It includes both real and synthetic Bengali speech samples, providing a comprehensive resource for train- ing and testing models in the field of audio forensics and deep- fake detection. 3.1. Da...

    work page

  4. [4]

    Later we explain how we used the trained model to create the Bangla Deepfake audio dataset

    Methodology In this section, we discuss the detailed methodology of training our model with the phonetically balanced SUST TTS Bangla- speech dataset. Later we explain how we used the trained model to create the Bangla Deepfake audio dataset. Finally, we com- plete the discussion by evaluating the quality of the generated Deepfake audio. Figure 1: Overvie...

    work page

  5. [5]

    Figure 2: t-SNE visualization of MFCC features for real and deepfake audio samples

    Evaluation Our dataset was evaluated through qualitative and quantita- tive measures to assess the effectiveness and robustness of the dataset for deepfake audio detection. Figure 2: t-SNE visualization of MFCC features for real and deepfake audio samples. Real audio samples are represented in blue circles, while deepfake audio samples are shown in red sq...

    work page

  6. [6]

    Does the fake speech sound natural and human-like?

    work page

  7. [7]

    The average Robust-MOS scores for these questions were 3.40, and 4.01, respectively

    Can you clearly understand the spoken content in the fake audio? To ensure a robust and unbiased evaluation, we excluded the highest and lowest scores for each question when calcu- lating the MOS, referred to as the Robust MOS. The average Robust-MOS scores for these questions were 3.40, and 4.01, respectively. These results confirm that our system achiev...

    work page

  8. [8]

    Conclusion In this paper, we introduced the Bengali Deepfake Audio Dataset for a single speaker (male), a resource designed to advance deepfake audio detection for Bangla. By employing state-of-the-art Text-to-Speech (TTS) techniques, we ensured that the synthetic audio retains high levels of naturalness and quality, making detection a challenging task. P...

    work page

Show all 35 references
  1. [9]

    Deepfakes: What are they and why would i make one?

    B. Bitesize, “Deepfakes: What are they and why would i make one?” BBC Bitesize , 2019. [Online]. Available: https://www.bbc.co.uk/bitesize/articles/zfkwcqt

    2019

  2. [10]

    Dataset pruning for resource-constrained spoofed audio detection,

    A. H. Azeemi, I. A. Qazi, and A. A. Raza, “Dataset pruning for resource-constrained spoofed audio detection,” in INTER- SPEECH, 2022, pp. 416–420

    2022

  3. [11]

    Exploring gaussian mixture model framework for speaker adaptation of deep neu- ral network acoustic models,

    N. Tomashenko, Y . Khokhlov, and Y . Est`eve, “Exploring gaussian mixture model framework for speaker adaptation of deep neu- ral network acoustic models,” arXiv preprint arXiv:2003.06894 , 2020

    2003 arXiv

  4. [12]

    An introduction to variational autoencoders,

    D. P. Kingma, M. Welling et al., “An introduction to variational autoencoders,” Foundations and Trends® in Machine Learning , vol. 12, no. 4, pp. 307–392, 2019

    2019

  5. [13]

    Fastspeech: Fast, robust and controllable text to speech,

    Y . Ren, Y . Ruan, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y . Liu, “Fastspeech: Fast, robust and controllable text to speech,” Advances in neural information processing systems, vol. 32, 2019

    2019

  6. [14]

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

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

    2020

  7. [15]

    Zmm-tts: Zero-shot multi- lingual and multispeaker speech synthesis conditioned on self- supervised discrete speech representations,

    C. Gong, X. Wang, E. Cooper, D. Wells, L. Wang, J. Dang, K. Richmond, and J. Yamagishi, “Zmm-tts: Zero-shot multi- lingual and multispeaker speech synthesis conditioned on self- supervised discrete speech representations,” IEEE/ACM Transac- tions on Audio, Speech, and Language...

    2024

  8. [16]

    Learning to speak from text: Zero-shot multi- lingual text-to-speech with unsupervised text pretraining,

    T. Saeki, S. Maiti, X. Li, S. Watanabe, S. Takamichi, and H. Saruwatari, “Learning to speak from text: Zero-shot multi- lingual text-to-speech with unsupervised text pretraining,” arXiv preprint arXiv:2301.12596, 2023

    2023 arXiv

  9. [17]

    Asvspoof 2021: accelerating progress in spoofed and deep- fake speech detection,

    J. Yamagishi, X. Wang, M. Todisco, M. Sahidullah, J. Patino, A. Nautsch, X. Liu, K. A. Lee, T. Kinnunen, N. Evans et al. , “Asvspoof 2021: accelerating progress in spoofed and deep- fake speech detection,” in ASVspoof 2021 Workshop-Automatic Speaker Verification and Spoofing C...

    2021

  10. [18]

    Add 2023: the second audio deepfake detection challenge,

    J. Yi, J. Tao, R. Fu, X. Yan, C. Wang, T. Wang, C. Y . Zhang, X. Zhang, Y . Zhao, Y . Renet al., “Add 2023: the second audio deepfake detection challenge,” arXiv preprint arXiv:2305.13774, 2023

    2023 arXiv

  11. [19]

    Comparative analysis of transfer learning in deep learning text-to-speech models on a few-shot, low-resource, customized dataset,

    Z. Liu, “Comparative analysis of transfer learning in deep learning text-to-speech models on a few-shot, low-resource, customized dataset,” arXiv preprint arXiv:2310.04982, 2023

    2023 arXiv

  12. [20]

    Fakeavceleb: A novel audio-video multimodal deepfake dataset,

    H. Khalid, S. Tariq, M. Kim, and S. S. Woo, “Fakeavceleb: A novel audio-video multimodal deepfake dataset,” arXiv preprint arXiv:2108.05080, 2021

    2021 arXiv

  13. [21]

    Livespeech: Low- latency zero-shot text-to-speech via autoregressive modeling of audio discrete codes,

    T. Dang, D. Aponte, D. Tran, and K. Koishida, “Livespeech: Low- latency zero-shot text-to-speech via autoregressive modeling of audio discrete codes,” arXiv preprint arXiv:2406.02897, 2024

    2024 arXiv

  14. [22]

    Neural codec language models are zero-shot text to speech synthesizers,

    C. Wang and Others, “Neural codec language models are zero-shot text to speech synthesizers,” in arXiv preprint arXiv:2301.02111, 2023, accessed: 11-Feb-2025. [Online]. Available: https://arxiv.org/abs/2301.02111

    2023 arXiv

  15. [23]

    Sust tts corpus: A phonetically-balanced corpus for bangla text-to-speech synthe- sis,

    A. Ahmad, M. Selim, M. Iqbal, and M. Rahman, “Sust tts corpus: A phonetically-balanced corpus for bangla text-to-speech synthe- sis,” Acoustical Science and Technology, vol. 42, pp. 326–332, 11 2021

    2021

  16. [24]

    Common voice: A massively-multilingual speech corpus,

    R. Ardila, M. Branson, K. Davis, M. Henretty, M. Kohler, J. Meyer, R. Morais, L. Saunders, F. M. Tyers, and G. Weber, “Common voice: A massively-multilingual speech corpus,” in Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), 2020, pp. 4211–4215

    2020

  17. [25]

    Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,

    J. Kim, J. Kong, and J. Son, “Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,” CoRR, vol. abs/2106.06103, 2021

    2021 arXiv

  18. [26]

    The lj speech dataset,

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

    2017

  19. [27]

    Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,

    J. Kim, J. Kong, and J. Son, “Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,” in Inter- national Conference on Machine Learning . PMLR, 2021, pp. 5530–5540

    2021

  20. [28]

    An introduction to variational infer- ence,

    A. Ganguly and S. W. Earp, “An introduction to variational infer- ence,” arXiv preprint arXiv:2108.13083, 2021

    2021 arXiv

  21. [29]

    Glow-tts: A genera- tive flow for text-to-speech via monotonic alignment search,

    J. Kim, S. Kim, J. Kong, and S. Yoon, “Glow-tts: A genera- tive flow for text-to-speech via monotonic alignment search,”Ad- vances in Neural Information Processing Systems , vol. 33, pp. 8067–8077, 2020

    2020

  22. [30]

    Short term spectral analysis, synthesis, and modification by discrete fourier transform,

    J. Allen, “Short term spectral analysis, synthesis, and modification by discrete fourier transform,” IEEE transactions on acoustics, speech, and signal processing, vol. 25, no. 3, pp. 235–238, 1977

    1977

  23. [31]

    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

  24. [32]

    Flow++: Improving flow-based generative models with variational dequan- tization and architecture design,

    J. Ho, X. Chen, A. Srinivas, Y . Duan, and P. Abbeel, “Flow++: Improving flow-based generative models with variational dequan- tization and architecture design,” in International conference on machine learning. PMLR, 2019, pp. 2722–2730

    2019

  25. [33]

    Common voice: A massively-multilingual speech corpus,

    R. Ardila, M. Branson, K. Davis, M. Henretty, M. Kohler, J. Meyer, R. Morais, L. Saunders, F. M. Tyers, and G. Weber, “Common voice: A massively-multilingual speech corpus,”arXiv preprint arXiv:1912.06670, 2019

    1912 arXiv

  26. [34]

    Mean opinion score (mos) revis- ited: methods and applications, limitations and alternatives,

    R. C. Streijl and D. S. Hands, “Mean opinion score (mos) revis- ited: methods and applications, limitations and alternatives,”Mul- timedia Systems, vol. 22, no. 2, pp. 213–227, 2016

    2016

  27. [35]

    Visualizing data using t-sne,

    L. Van der Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of machine learning research, vol. 9, no. 11, 2008

    2008

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