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REVIEW 4 major objections 4 minor 1 cited by

Scale This, Not That: Investigating Key Dataset Attributes for Efficient Speech Enhancement Scaling

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper claims that when scaling training data for speech enhancement, acoustic attributes such as speaker and noise diversity matter far more than semantic attributes such as language and text, and demonstrates this with a…

desk verdict A useful controlled-study framework for SE data scaling, but the 'language-independence' claim is partly an artifact of English-voice TTS prompts and single runs. read the letter →

arxiv 2412.14890 v1 pith:OAROWUED submitted 2024-12-19 eess.AS cs.SD

Leying Zhang , Wangyou Zhang , Chenda Li , Yanmin Qian This is my paper · ORCID
classification eess.AScs.SD
keywords speechenhancementdatasetscalingsyntheticzero-shottext-to-speechspeakerdiversitynoiselanguagevariabilitytext
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 argues that when scaling up training data for speech enhancement, not all diversity is equal: acoustic variation in speakers and noise matters far more than semantic variation in text and language. To test this, the authors build a generation-training-evaluation pipeline using a multi-lingual zero-shot text-to-speech model, synthesizing training corpora in which only one attribute (text, language, speaker, or noise) changes at a time. They train both a discriminative (BSRNN) and a generative (SGMSE) enhancement model on each controlled corpus and evaluate on real multilingual test sets. Their results suggest that models trained on a single sentence or a single language perform nearly as well as models trained on rich text and language diversity, whereas reducing speaker or noise diversity hurts performance. If correct, this gives data collectors a concrete priority: spend scaling budgets on speaker and noise coverage, not on language or textual breadth.

What carries the argument

The load-bearing mechanism is a generation-training-evaluation pipeline built on a pre-trained multi-lingual zero-shot text-to-speech model. The TTS model takes a short speaker prompt and a text transcription and produces speech in a chosen language with a controlled speaker identity, so the authors can synthesize corpora in which only the target attribute varies. They generate datasets varying the number of unique transcriptions, the number of languages, the number of speakers (in single-prompt and multi-prompt modes), and the noise duration or noise-type count, then train BSRNN and SGMSE on fixed noisy-clean mixtures simulated from paired clean speech and environmental noise. The comparison of model performance across these controlled corpora is what attributes the observed scaling effects to the manipulated attribute rather than to confounded differences in content.

What would settle it

Run the same language-scaling experiment with real speech from native speakers recorded in each language while holding speaker identity and noise distribution fixed, and test on held-out languages: if PESQ, STOI, SDR, or DNSMOS drop substantially as the number of languages increases from one to ten, the language-independence claim would be contradicted. A complementary check is to compare the performance of models trained on a real multilingual corpus against models trained on the synthetic corpus under identical evaluation; a large gap would indicate that the synthetic proxy is not faithful.

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Extended reading notes

Core claim

The central claim is that current speech enhancement models are largely text- and language-independent while being sensitive to speaker and noise diversity. Using purely synthetic speech generated by a zero-shot text-to-speech model, the authors manipulate one dataset attribute at a time while holding total duration and word counts roughly constant. Across both a discriminative model (band-split RNN) and a generative model (diffusion-based), they find that collapsing textual diversity to a single sentence costs little in PESQ, STOI, SDR, and DNSMOS on in-domain and out-of-domain multilingual evaluations, and that adding languages up to ten languages does not improve or degrade performance much. In contrast, increasing the number of speakers and the variety of speaker prompts improves enhancement, and increasing noise type diversity helps generalization to unseen noise, especially for the discriminative model. The finding is framed as guidance for efficient dataset scaling: spend resources on acoustic attribute diversity first.

Load-bearing premise

The whole ranking of attributes rests on whether speech produced by a synthetic voice behaves like real speech for scaling experiments; if TTS artifacts or non-native voice prompts distort the true language or speaker effects, the ranking could change.

Editorial extensions

If this is right

  • Data scaling budgets for speech enhancement should prioritize adding speakers and noise types over adding text or language coverage.
  • Training on synthetic speech is a viable proxy for real speech when studying scaling laws, at least for the models and test conditions examined.
  • A model trained almost entirely on a single-sentence, single-language corpus can generalize to multilingual, varied-content test conditions, so small-domain synthetic corpora may be sufficient for many enhancement deployments.
  • Improving noise-type diversity in training data is a more effective route to generalization on unseen noise than simply increasing noise duration.
  • For generative models, the effect of speaker and noise diversity is visible but weaker than for discriminative models, implying separate scaling strategies for the two model families.

Reading between the lines

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

  • Editorial inference: the language-independence result may be specific to speech enhancement, which does not need to understand content; tasks like ASR or translation would likely show a much larger language and text effect, so the ranking of attributes should not be transferred across tasks.
  • Editorial inference: because the multilingual synthetic speech was produced from English speaker prompts, the language comparison may understate acoustic differences between languages; a test using native speaker prompts per language would clarify whether the TTS proxy masked a real language effect.
  • Editorial inference: a natural next experiment is to apply the same controlled-generation framework to real human speech where possible, or to measure how far the synthetic-to-real gap grows as TTS quality degrades, since the entire argument depends on TTS preserving real-data scaling behavior.
  • Editorial inference: the saturation of speaker gains beyond 100 speakers in this setup may reflect the fixed total data size; with a larger utterance budget, speaker gains might continue further, so the '100 speakers' number should not be read as a universal ceiling.
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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 / 4 minor

Summary. The paper proposes a generation-training-evaluation framework that uses a zero-shot multilingual TTS system (XTTS) to synthesize speech enhancement training corpora in which text, language, speaker, and noise attributes are varied independently. The authors train two representative SE models (BSRNN and SGMSE) on these controlled synthetic datasets and evaluate them on LibriMix plus multilingual out-of-domain test sets. Their main empirical finding is that acoustic attributes (speaker and noise) matter much more than semantic attributes (text and language) for current SE models, and they conclude that data scaling budgets should prioritize speaker and noise diversity. The paper also reports that purely synthetic TTS-based training data performs comparably to real speech data.

Significance. If the central ranking of attribute importance holds, the paper provides actionable guidance for SE data collection and augmentation: spend scaling resources on speaker and noise diversity rather than on text or language coverage. The study is valuable for its controlled experimental design, the use of two model families (discriminative and generative), evaluation on external multilingual and out-of-domain noise sets, and the plan to open-source the generation code. The result that purely synthetic TTS data can train competitive SE models is itself a useful contribution. However, the headline claim that models are 'largely text- and language-independent' is only as strong as the synthetic proxy used to measure language scaling, and the paper's own cross-lingual transfer results complicate the claim. The lack of repeated runs also limits the certainty of comparisons between flat and rising scaling curves.

major comments (4)
  1. [Section III-A, Table I, Section IV-A] The language manipulation uses speaker prompts extracted from LibriSpeech for every utterance, including non-English languages. This means Chinese, Czech, German, and other languages are spoken by cloned English voices, which can introduce non-native accents or TTS artifacts that flatten genuine phonological and phonetic differences across languages. The only synthetic-to-real validation (Section IV-A, Table II) compares average LibriMix quality between models trained on real versus synthetic data; it does not validate that language scaling slopes measured on synthetic data transfer to real multilingual data. To support the abstract's claim that language is much less important than acoustic attributes, the authors should either validate the language scaling with native-prompt or real multilingual training data, or substantially soften the claim.
  2. [Section IV-C, Figure 2] Figure 2 shows that SGMSE trained only on English generalizes poorly to Chinese, and the text states that 'the performance of the generative model varies based on the training language.' This is difficult to reconcile with the abstract's statement that models are 'largely language-independent.' At minimum, the paper should distinguish between (a) adding language diversity during training and (b) zero-shot cross-lingual transfer, and should explain why poor English-to-Chinese transfer does not contradict the claimed unimportance of the language attribute. As written, the paper's own evidence suggests that language can matter for generative models.
  3. [Figures 1-4] Every data point in the scaling curves comes from a single training run with no error bars, repeated seeds, or significance tests. The central conclusion relies on distinguishing flat curves (text, language) from increasing curves (speaker, noise), and on small differences in some conditions. Without variance estimates, it is impossible to assess whether the observed flatness is meaningful or within run-to-run noise. The authors should provide at least three seeds for key comparisons or report confidence intervals.
  4. [Section III-A, Figure 1(e,f)] The speaker diversity experiments keep the total number of utterances m fixed while varying the number of speakers s. Consequently, increasing s reduces the number of utterances per speaker, confounding speaker diversity with the degree of repeated exposure to each speaker's voice. The observed saturation beyond 100 speakers may reflect per-speaker data scarcity rather than a true limit of speaker diversity. This confound should be controlled or explicitly discussed.
minor comments (4)
  1. [Figure 2 caption] The caption contains a typo: 'Evaluaion' should be 'Evaluation.'
  2. [Table I] The abbreviations in the table, such as 'W,T' and '#NT', are not defined in the caption; please define them in a footnote or in the table caption.
  3. [Section IV-C] The sentence 'we may conclude that it is safe to scale the SE training data by introducing new languages' is too strong given the cross-lingual transfer results in Figure 2; a more cautious formulation would reflect the observed discrepancy between discriminative and generative models.
  4. [Section I] The contribution list says the analysis reveals that models are 'largely text- and language-independent,' but this phrasing is already an interpretation before the experimental results are presented; consider rephrasing to state the finding after the experiments.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the attribute-scaling conclusions are empirical, externally evaluated, and not forced by construction.

full rationale

The paper's central claim—that acoustic attributes (speaker, noise) matter more than semantic attributes (text, language) for speech enhancement scaling—is an empirical finding derived from controlled synthetic-data experiments, not a quantity fitted from the evaluation data. The generation-training-evaluation pipeline manipulates one attribute at a time (Section III-A) and evaluates on external real test sets (LibriMix, CommonVoice with WHAM!, Freesound, and AudioSet noises) with four metrics (Section III-C). The validity of synthetic speech is checked on the LibriMix test set against real speech training (Section IV-A, Table II), so the subsequent attribute analyses do not assume the conclusion. Self-citations (e.g., [4], [29]) are used only for background motivation and the ESPnet-SE training toolkit; they are not invoked as proof of any attribute ranking. The strongest limitation is a construct-validity concern rather than circularity: the language manipulation in Section III-A uses speaker prompts extracted from LibriSpeech for every language, so XTTS clones English voices to speak non-English text, which may attenuate genuine language effects, and the paper's own Figure 2 shows SGMSE trained on English generalizes poorly to Chinese. That inconsistency makes the abstract's 'largely language-independent' claim overstated, but the result is not equivalent to its inputs by construction and no fitted parameter is renamed as a prediction. The scaling curves are direct measurements, so the circularity burden is low.

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

The central claim rests on the suitability of XTTS synthetic speech as a proxy for real speech, on the independence of the attribute controls, on the evaluation metrics chosen, and on the representativeness of single training runs. No parameters are fitted to produce the conclusion; all numerical settings are fixed experimental choices.

assumptions (4)
  • domain assumption Zero-shot TTS (XTTS) output is a faithful proxy for real speech when training speech enhancement models.
    Section III-A generates all attribute-controlled datasets with XTTS. Section IV-A validates synthetic versus real data only on the LibriMix test set, so broader proxy validity is assumed.
  • domain assumption The generation pipeline isolates each attribute independently, e.g., changing language while keeping speaker prompts and duration fixed.
    Section III-A claims precise independent control via prompting. Non-English speech is produced from English LibriSpeech speaker prompts, which removes natural covariation between language and native speaker characteristics.
  • domain assumption Objective metrics (PESQ, STOI, SDR, DNSMOS) and the four evaluation sets capture the speech enhancement performance relevant to the conclusion.
    Section III-C uses these metrics on LibriMix and three out-of-domain multilingual sets. No listening test or uncertainty quantification is provided.
  • domain assumption Fixed hyperparameters and a single training run per condition are representative enough to compare scaling trends.
    Section III-B fixes all hyperparameters for every experiment. No seed variation or error bars are reported, so the stability of the trends is assumed.

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Cite this review

Pith. "Pith review of Scale This, Not That: Investigating Key Dataset Attributes for Efficient Speech Enhancement Scaling." pith.science (2026). https://pith.science/paper/OAROWUED

@misc{pith2026241214890,
  author       = {Pith},
  title        = {Pith review of: Scale This, Not That: Investigating Key Dataset Attributes for Efficient Speech Enhancement Scaling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OAROWUED}},
  note         = {Machine review of arXiv:2412.14890}
}
read the original abstract

Recent speech enhancement models have shown impressive performance gains by scaling up model complexity and training data. However, the impact of dataset variability (e.g. text, language, speaker, and noise) has been underexplored. Analyzing each attribute individually is often challenging, as multiple attributes are usually entangled in commonly used datasets, posing a significant obstacle in understanding the distinct contributions of each attribute to the model's performance. To address this challenge, we propose a generation-training-evaluation framework that leverages zero-shot text-to-speech systems to investigate the impact of controlled attribute variations on speech enhancement performance. It enables us to synthesize training datasets in a scalable manner while carefully altering each attribute. Based on the proposed framework, we analyze the scaling effects of various dataset attributes on the performance of both discriminative and generative SE models. Extensive experiments on multi-domain corpora imply that acoustic attributes (e.g., speaker and noise) are much more important to current speech enhancement models than semantic attributes (e.g., language and text), offering new insights for future research.

Figures

Figures reproduced from arXiv: 2412.14890 by the authors.

Figure 1
Figure 1. Analysis of data variability on text, language and speaker for both discriminative (BSRNN) and generative (SGMSE) speech enhancement models. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Analysis of prompt variability for given fixed number of speakers [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 2
Figure 2. Evaluaion on 10 different languages across models trained with [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Analysis of effects of the noise type and noise duration [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]

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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. Full citation record

  1. Less is More: Data Curation Matters in Scaling Speech Enhancement

    eess.AS 2025-06 conditional novelty 5.0 of 10

    A quality-filtered 700-hour subset beats the full 2,500-hour URGENT2025 training set on perceptual quality metrics for both discriminative and generative speech enhancement models.

Reference graph

Works this paper leans on

36 extracted references · 26 canonical work pages · cited by 1 Pith paper

  1. [1]

    P. C. Loizou, Speech enhancement: theory and practice . CRC press, 2007

    2007

  2. [2]

    Toward universal speech enhancement for diverse input conditions,

    W. Zhang, K. Saijo, Z.-Q. Wang, S. Watanabe, and Y . Qian, “Toward universal speech enhancement for diverse input conditions,” in Proc. ASRU, 2023, pp. 1–6

    work page 2023

  3. [3]

    Improving design of input condition invariant speech enhancement,

    W. Zhang, J.-w. Jung, and Y . Qian, “Improving design of input condition invariant speech enhancement,” in Proc. ICASSP , 2024, pp. 10 696– 10 700

    work page 2024

  4. [4]

    Beyond performance plateaus: A comprehensive study on scalability in speech enhancement,

    W. Zhang, K. Saijo, J.-w. Jung, C. Li, S. Watanabe, and Y . Qian, “Beyond performance plateaus: A comprehensive study on scalability in speech enhancement,” in Proc. Interspeech, 2024, pp. 1740–1744

    work page 2024

  5. [5]

    Complexity scaling for speech denoising,

    H. Chen, J. Yu, and C. Weng, “Complexity scaling for speech denoising,” in Proc. ICASSP, 2024, pp. 12 276–12 280

    work page 2024

  6. [6]

    The effect of training dataset size on discriminative and diffusion-based speech enhancement systems,

    P. Gonzalez, Z.-H. Tan, J. Østergaard, J. Jensen, T. S. Alstrøm, and T. May, “The effect of training dataset size on discriminative and diffusion-based speech enhancement systems,” IEEE Signal Processing Letters, vol. 31, pp. 2225–2229, 2024

    work page 2024

  7. [7]

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

    Z. Ju, Y . Wang, K. Shen, X. Tan, D. Xin, D. Yang, Y . Liu, Y . Leng, K. Song, S. Tang et al. , “NaturalSpeech 3: Zero-shot speech synthesis with factorized codec and diffusion models,” in Proc. ICML, 2024

    work page 2024

  8. [8]

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

    C. Wang, S. Chen, Y . Wu, Z. Zhang, L. Zhou, S. Liu, Z. Chen, Y . Liu, H. Wang, J. Li et al., “Neural codec language models are zero-shot text to speech synthesizers,” arXiv preprint arXiv:2301.02111 , 2023

    arXiv 2023

Show all 36 references
  1. [9]

    V oicebox: Text- guided multilingual universal speech generation at scale,

    M. Le, A. Vyas, B. Shi, B. Karrer, L. Sari, R. Moritz, M. Williamson, V . Manohar, Y . Adi, J. Mahadeokar, and W.-N. Hsu, “V oicebox: Text- guided multilingual universal speech generation at scale,” in Advances in Neural Information Processing Systems , vol. 36, 2024, pp. 14 0...

    2024

  2. [10]

    XTTS: A massively multilingual zero-shot text-to-speech model,

    E. Casanova, K. Davis, E. G ¨olge, G. G ¨oknar, I. Gulea, L. Hart, A. Alja- fari, J. Meyer, R. Morais, S. Olayemi, and J. Weber, “XTTS: A massively multilingual zero-shot text-to-speech model,” in Proc. Interspeech, 2024, pp. 4978–4982

    2024

  3. [11]

    Seed-TTS: A family of high-quality versatile speech generation models,

    P. Anastassiou, J. Chen, J. Chen, Y . Chen, Z. Chen, Z. Chen, J. Cong, L. Deng, C. Ding, L. Gao et al. , “Seed-TTS: A family of high-quality versatile speech generation models,” arXiv preprint arXiv:2406.02430 , 2024

    2024 arXiv

  4. [12]

    BASE TTS: Lessons from building a billion-parameter text-to-speech model on 100k hours of data,

    M. Łajszczak, G. C ´ambara, Y . Li, F. Beyhan, A. van Korlaar, F. Yang, A. Joly, ´A. Mart´ın-Cortinas, A. Abbas, A. Michalski et al., “BASE TTS: Lessons from building a billion-parameter text-to-speech model on 100k hours of data,” arXiv preprint arXiv:2402.08093 , 2024

    2024 arXiv

  5. [13]

    SynthASR: Unlocking synthetic data for speech recogni- tion,

    A. Fazel, W. Yang, Y . Liu, R. Barra-Chicote, Y . Meng, R. Maas, and J. Droppo, “SynthASR: Unlocking synthetic data for speech recogni- tion,” in Proc. Interspeech, 2021, pp. 896–900

    2021

  6. [14]

    Deep generative models for synthetic data: A survey,

    P. Eigenschink, T. Reutterer, S. Vamosi, R. Vamosi, C. Sun, and K. Kalcher, “Deep generative models for synthetic data: A survey,”IEEE Access, vol. 11, pp. 47 304–47 320, 2023

    2023

  7. [15]

    On the effect of purely synthetic training data for different automatic speech recognition archi- tectures,

    N. Rossenbach, B. Hilmes, and R. Schl ¨uter, “On the effect of purely synthetic training data for different automatic speech recognition archi- tectures,” arXiv preprint arXiv:2407.17997 , 2024

    2024 arXiv

  8. [16]

    SynAug: Synthesis- based data augmentation for text-dependent speaker verification,

    C. Du, B. Han, S. Wang, Y . Qian, and K. Yu, “SynAug: Synthesis- based data augmentation for text-dependent speaker verification,” in Proc. ICASSP, 2021, pp. 5844–5848

    2021

  9. [17]

    Speech enhancement using deep learning methods: A review,

    A. Yuliani, M. F. Amri, E. Suryawati, A. Ramdan, and H. Pardede, “Speech enhancement using deep learning methods: A review,” Jurnal Elektronika dan Telekomunikasi , vol. 21, no. 1, pp. 19–26, 2021

    2021

  10. [18]

    Conv-TasNet: Surpassing ideal time– frequency magnitude masking for speech separation,

    Y . Luo and N. Mesgarani, “Conv-TasNet: Surpassing ideal time– frequency magnitude masking for speech separation,” IEEE/ACM trans- actions on audio, speech, and language processing , vol. 27, no. 8, pp. 1256–1266, 2019

    2019

  11. [19]

    Music source separation with band-split RNN,

    Y . Luo and J. Yu, “Music source separation with band-split RNN,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 31, pp. 1893–1901, 2023

    1901

  12. [20]

    Speech enhancement and dereverberation with diffusion-based genera- tive models,

    J. Richter, S. Welker, J.-M. Lemercier, B. Lay, and T. Gerkmann, “Speech enhancement and dereverberation with diffusion-based genera- tive models,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 2351–2364, 2023

    2023

  13. [21]

    DDTSE: Discriminative diffusion model for target speech extraction,

    L. Zhang, L. Y . Yao Qian, H. Wang, H. Yang, S. Liu, L. Zhou, and Y . Qian, “DDTSE: Discriminative diffusion model for target speech extraction,” Proc. SLT, 2024

    2024

  14. [22]

    Conditional diffusion probabilistic model for speech enhancement,

    Y .-J. Lu, Z.-Q. Wang, S. Watanabe, A. Richard, C. Yu, and Y . Tsao, “Conditional diffusion probabilistic model for speech enhancement,” in Proc. ICASSP, 2022, pp. 7402–7406

    2022

  15. [23]

    Diffusion-based generative modeling with discriminative guidance for streamable speech enhance- ment,

    C. Li, S. Cornell, S. Watanabe, and Y . Qian, “Diffusion-based generative modeling with discriminative guidance for streamable speech enhance- ment,” arXiv preprint arXiv:2406.13471 , 2024

    2024 arXiv

  16. [24]

    Librimix: An open-source dataset for generalizable speech separation,

    J. Cosentino, M. Pariente, S. Cornell, A. Deleforge, and E. Vincent, “Librimix: An open-source dataset for generalizable speech separation,” arXiv preprint arXiv:2005.11262 , 2020

    2005 arXiv

  17. [25]

    Librispeech: an ASR corpus based on public domain audio books,

    V . Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: an ASR corpus based on public domain audio books,” in Proc. ICASSP , 2015, pp. 5206–5210

    2015

  18. [26]

    WHAM!: Extending speech separation to noisy environments,

    G. Wichern, J. Antognini, M. Flynn, L. R. Zhu, E. McQuinn, D. Crow, E. Manilow, and J. Le Roux, “WHAM!: Extending speech separation to noisy environments,” in Proc. Interspeech, 2019, pp. 1368–1372

    2019

  19. [27]

    TUT database for acoustic scene classification and sound event detection,

    A. Mesaros, T. Heittola, and T. Virtanen, “TUT database for acoustic scene classification and sound event detection,” in 24th European Signal Processing Conference (EUSIPCO) , 2016, pp. 1128–1132

    2016

  20. [28]

    Common voice: A massively-multilingual speech corpus,

    R. Ardila, M. Branson, K. Davis, M. Kohler, J. Meyer, M. Henretty, R. Morais, L. Saunders, F. Tyers, and G. Weber, “Common voice: A massively-multilingual speech corpus,” in Proceedings of the 12th Language Resources and Evaluation Conference , 2020, pp. 4218–4222

    2020

  21. [29]

    ESPnet- SE: End-to-end speech enhancement and separation toolkit designed for ASR integration,

    C. Li, J. Shi, W. Zhang, A. S. Subramanian, X. Chang, N. Kamo, M. Hira, T. Hayashi, C. Boeddeker, Z. Chen, and S. Watanabe, “ESPnet- SE: End-to-end speech enhancement and separation toolkit designed for ASR integration,” in Proc. SLT, 2021, pp. 785–792

    2021

  22. [30]

    Freesound datasets: A platform for the creation of open audio datasets,

    E. Fonseca, J. Pons Puig, X. Favory, F. Font Corbera, D. Bogdanov, A. Ferraro, S. Oramas, A. Porter, and X. Serra, “Freesound datasets: A platform for the creation of open audio datasets,” in Proc. ISMIR, 2017, pp. 486–493

    2017

  23. [31]

    Audio set: An ontology and human- labeled dataset for audio events,

    J. F. Gemmeke, D. P. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, “Audio set: An ontology and human- labeled dataset for audio events,” in Proc. ICASSP, 2017, pp. 776–780

    2017

  24. [32]

    Perceptual evaluation of speech quality (pesq)-a new method for speech quality assessment of telephone networks and codecs,

    A. W. Rix, J. G. Beerends, M. P. Hollier, and A. P. Hekstra, “Perceptual evaluation of speech quality (pesq)-a new method for speech quality assessment of telephone networks and codecs,” in Proc. ICASSP, vol. 2, 2001, pp. 749–752

    2001

  25. [33]

    An algorithm for predicting the intelligibility of speech masked by modulated noise maskers,

    J. Jensen and C. H. Taal, “An algorithm for predicting the intelligibility of speech masked by modulated noise maskers,” IEEE/ACM Transac- tions on Audio, Speech, and Language Processing , vol. 24, no. 11, pp. 2009–2022, 2016

    2009

  26. [34]

    Performance measurement in blind audio source separation,

    E. Vincent, R. Gribonval, and C. F ´evotte, “Performance measurement in blind audio source separation,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 14, no. 4, pp. 1462–1469, 2006

    2006

  27. [35]

    DNSMOS: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,

    C. K. Reddy, V . Gopal, and R. Cutler, “DNSMOS: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,” in Proc. ICASSP, 2021, pp. 6493–6497

    2021

  28. [36]

    Comparative intelligibility investigation of single-channel noise-reduction algorithms for Chinese, Japanese, and English,

    J. Li, L. Yang, J. Zhang, Y . Yan, Y . Hu, M. Akagi, and P. C. Loizou, “Comparative intelligibility investigation of single-channel noise-reduction algorithms for Chinese, Japanese, and English,” The Journal of the Acoustical Society of America , vol. 129, no. 5, pp. 3291– 3301, 2011

    2011

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