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REVIEW 4 major objections 3 minor 2 cited by

Maestro-EVC: Controllable Emotional Voice Conversion Guided by References and Explicit Prosody

T0 review · 4 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Maestro-EVC independently controls content, speaker identity, and emotion in emotional voice conversion using three separate reference utterances, and explicitly transfers pitch, energy, and rhythm so temporal emotion dynamics survive proso

desk verdict Solid EVC system with explicit prosody transfer, but the mismatch-robustness claim is not actually tested and the pitch+energy novelty claim is false. read the letter →

arxiv 2508.06890 v1 pith:PWPMUTIG submitted 2025-08-09 cs.SD cs.AIcs.CLeess.AS

Jinsung Yoon , Wooyeol Jeong , Jio Gim , Young-Joo Suh This is my paper
classification cs.SDcs.AIcs.CLeess.AS
keywords emotionalvoiceconversioncontrollablesynthesisprosodymodelingtemporalemotionrepresentationreference-guidedgenerationdisentangledaugmentationzero-shot
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

Maestro-EVC is an emotional voice conversion framework that lets a user choose the spoken content, the target voice, and the emotional style from three different reference utterances, then synthesizes a single utterance that says the content in the target voice with the target emotion. The central claim is that these three attributes can be disentangled rather than merely mixed: a temporal emotion encoder aligned to content by cross-attention, an explicit transfer of F0, energy, and duration from the emotion reference, and a speaker encoder trained to suppress emotion keep their channels separate. Because the content and emotion references usually differ in both text and emotional style, the paper adds a prosody augmentation step that randomly shifts and piecewise time-warps smoothed F0 and energy, letting the model learn to adapt prosody instead of copying it. The paper reports that Maestro-EVC beats StyleVC and ZEST on every objective and subjective metric they measure, including zero-shot tests on unseen speakers and unseen emotions. If that holds, it makes reference-guided emotional dubbing practical.

What carries the argument

The load-bearing machinery has three parts. TCEM (temporal content-aware emotion modeling) uses cross-attention with content units as queries and frame-level emotion embeddings as keys and values, then applies a gradient reversal layer and content classifier to strip residual phonetic cues from the emotion representation. EEPT (explicit emotion prosody transfer) smooths F0, energy, and duration from the emotion reference, augments F0 and energy during training by random shifting (up to 15 frames) and piecewise time warping (2-5 segments, scale 0.4-1.6), and trains an FE predictor conditioned on the augmented prosody, content units, and VUV mask, together with a duration predictor. EISE (emot

What would settle it

Take a content reference with very slow, flat prosody and an emotion reference with very fast, jagged surprise prosody, far beyond the augmentation ranges (shifts larger than 15 frames, segment scaling outside 0.4-1.6), and measure WER and F0-PCC of the output. If naturalness and content fidelity collapse while F0-PCC stays high, the model is copying rather than adapting prosody, and the augmentation envelope is the true boundary of the claim.

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Extended reading notes

Core claim

At inference, Maestro-EVC takes three references: $x_c$ for content, $x_e$ for emotion, and $x_s$ for speaker, which are identical during reconstruction training but arbitrary at inference. The content encoder turns HuBERT-discretized units of $x_c$ into a sequence; the temporal emotion encoder produces frame-level emotion embeddings that TCEM aligns to the content units through cross-attention, with a gradient reversal layer and content classifier removing residual linguistic cues. EEPT smooths F0, energy, and duration from $x_e$ with a Savitzky-Golay filter, augments F0 and energy during training by random shifts and piecewise time warping, and conditions an FE predictor on the augmented p

Load-bearing premise

The load-bearing premise is that randomly shifting and time-warping smoothed F0 and energy during training covers the distribution of real prosody mismatches between a content reference and an emotion reference; if a real mismatch falls outside that augmented range, the learned transfer may force the wrong prosody onto the content and become unnatural.

Editorial extensions

If this is right

  • An operator can independently choose who speaks, what is said, and how it is said from three separate references, enabling emotional dubbing without requiring parallel recordings of the same sentence in every emotion.
  • Fine-grained temporal emotion, carried by F0 contours, energy, and rhythm, transfers from the emotion reference even when that reference differs in both linguistic content and emotion category from the content reference.
  • The model generalizes to unseen speakers and unseen emotion states, as evaluated on VCTK speakers and held-out emotion classes from CREMA-D and IEMOCAP.
  • The explicit prosody conditioning plus augmentation makes training possible under a reconstruction objective, so the model does not need matched emotional-prosody training pairs.
  • Output speech retains content intelligibility, with lower word and character error rates than the baselines, so the conversion is usable as a front end for downstream speech applications.

Reading between the lines

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

  • An implication the paper leaves implicit is that the augmentation ranges define the model's robustness envelope: the model's tolerance for prosody mismatch is only as wide as the shifts and warps seen in training, so those ranges are a direct lever for controlling where transfer starts to fail.
  • The same three-reference recipe could be applied to dimensions beyond categorical emotion, such as speaking style, emphasis, or prosodic caricature, by replacing the emotion encoder with any frame-level style extractor while keeping TCEM and EEPT fixed.
  • Because training only requires one utterance to serve as content, emotion, and speaker reference, the method could be trained on non-parallel, low-resource emotional speech; the zero-shot results hint at this flexibility but the paper does not claim it as a contribution.
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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 / 3 minor

Summary. Maestro-EVC proposes a reference-guided emotional voice conversion framework with three separate references for content, speaker, and emotion. It combines HuBERT discrete units for content, a pretrained temporal emotion encoder with cross-attention (TCEM), an explicit F0/energy/duration transfer module (EEPT) with a prosody augmentation scheme, and a speaker encoder with GRL and triplet loss (EISE). The model is trained reconstruction-style with xc=xe=xs and evaluated against StyleVC and ZEST on ESD, including zero-shot unseen speakers/emotions, using objective metrics and MOS. The paper reports consistent improvements over baselines and provides ablations for each main component.

Significance. The proposed architecture is a reasonable engineering contribution; if the results are robust, explicit prosody conditioning plus temporal emotion representations would be a useful step for controllable EVC. The ablations are well structured, the zero-shot evaluation on unseen speakers and emotions is a plus, and the demo page supports the claims. However, the central robustness claim about prosody-mismatched transfer is supported only by self-reconstruction training with on-utterance augmentations, and the objective comparison lacks significance testing; these issues need to be resolved before the claimed advantage can be fully credited.

major comments (4)
  1. [II.C.2 / Fig. 2] The paper claims that ProAug 'simulates prosody-mismatched conditions' and yields robust transfer 'even under prosody-mismatched conditions,' but during training all three references are the same utterance (Fig. 2, red dashed line). ProAug only applies random shifts and piecewise warps to the same utterance's smoothed F0/energy. Genuine inference mismatches involve different text, speaker, and emotion, whose pitch register, contour dynamics, and cross-speaker statistics are not represented by these on-utterance perturbations. The 'w/o Prosody Augmentation' ablation (Table I) shows only that this self-augmentation helps; it does not demonstrate coverage of the true mismatch distribution. To support the load-bearing claim, the authors should add an experiment that trains or evaluates under genuine cross-reference prosody pairs (e.g., content and emotion references from different speakers/e
  2. [Tables I and II] All objective metrics are reported as point estimates with no error bars, confidence intervals, or significance tests, although the paper asserts 'consistently surpasses the baselines on all metrics.' Several differences are small (e.g., SCA 93.69 vs. 93.54; E-PCC 0.316 vs. 0.293) and may be within run-to-run noise. The 700 test sets should permit bootstrap confidence intervals or paired tests. Without this, the claimed uniform superiority is not statistically supported. This is a central issue because the main conclusion rests on the comparison in Tables I–III.
  3. [II.C / Eq. (2) / Table I] F0-PCC and E-PCC are measured between the synthesized speech and the emotion reference whose F0/energy are directly given as inputs to the FE predictor (Eq. 2). The baselines StyleVC and ZEST do not receive explicit F0; they predict it from latent content/speaker/emotion embeddings. Thus the large F0-PCC gap (0.551 vs. 0.432/0.380) partly reflects a by-construction advantage, not necessarily better disentangled transfer. Please add a control baseline that also receives the reference F0/energy (e.g., a copy-prosody condition) and report correlation with content-natural prosody as well. The current interpretation in §IV.A overstates the evidence.
  4. [II.C.4 / Fig. 2] The duration predictor is never exposed to mismatched durations during training: since xc=xe, the smoothed durations ds always equal the content utterance's own durations. The prosody augmentation described in §II.C.2 applies only to F0 and energy (Eq. 1), not to duration. At inference, however, ds comes from a different utterance with potentially very different unit counts and speaking rate. The model therefore has no training signal for aligning ds with the content unit sequence. This is a second, concrete instance of the mismatch-robustness gap and should be addressed directly (e.g., augment durations as well, or supervise with cross-utterance duration pairs).
minor comments (3)
  1. [III.A.3 / III.B] The zero-shot evaluation on unseen emotions is described in one sentence; please report the number of held-out classes, the number of test utterances per class, and the construction of the test sets. This would help assess the strength of the generalization claim.
  2. [IV.D / Fig. 3] Figure 3 shows only a single example. Quantitative support (e.g., averaged F0 correlation across the test set, or several representative examples covering different emotion pairs) would make the explicit-prosody-transfer claim more convincing.
  3. [Reproducibility] No code or pre-trained models are provided. Releasing the implementation and pretrained checkpoints would substantially strengthen reproducibility and allow independent verification of the ablations and comparisons.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the explicit-F0 conditioning is a design choice, not a hidden fit, and load-bearing evidence uses external benchmarks.

full rationale

Maestro-EVC is an experimental systems paper with no formal derivation chain that could reduce to its inputs. The central claims (controllability, expressiveness, quality) are supported by external pretrained evaluators (Whisper, emotion2vec, ECAPA-based SCA) and by MOS, not by a parameter fitted to the target metric. The EEPT module is trained as a self-supervised denoiser: the model receives augmented F0/energy from the same utterance and predicts the original, which is a legitimate augmentation objective. The mismatch-robustness claim depends on whether random shift and piecewise warping adequately cover real mismatched-reference prosody—a generalization assumption, not a circular step. The F0-PCC metric is arguably lenient because the FE predictor is explicitly conditioned on reference F0 (Eq. 2), but this is the method's intended design: measuring how faithfully the conditioner is honored is a valid evaluation, and the paper also reports naturalness, emotion, and speaker metrics that do not reduce to the F0 input. There are no self-citations, uniqueness theorems, or ansatz-by-citation steps; all cited building blocks (HuBERT, ECAPA, HiFi-GAN, emotion2vec, Whisper) are external and independently established. The skeptical concern about mismatched reference distributions is a testable limitation, not an equivalence built into the formulation.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

This is an empirical system paper, so the 'free parameters' are training hyperparameters and hand-set augmentation ranges, not constants in a derivation. The key unvalidated assumption is that prosody augmentation covers the real mismatch distribution.

free parameters (5)
  • triplet margin alpha = 0.3
    Set by hand in Eq. (5); defines minimum cosine distance between positive and negative speaker pairs.
  • prosody augmentation shift range = [-15, 15] frames
    Random shift magnitude chosen by hand for the augmentation in §II.C.2.
  • piecewise warping parameters = 2-5 segments, scale [0.4, 1.6]
    Chosen by hand to simulate speaking-rate variation in §II.C.2.
  • loss weights = lambda_recon=45, others=1
    Tunable coefficients in Eq. (9); reconstruction weight set high to stabilize training.
  • content vocabulary size = 500
    K-means clusters for HuBERT units in §III.A.2; affects granularity of content representation.
assumptions (5)
  • domain assumption HuBERT discrete units represent linguistic content sufficient for reconstruction and prosody prediction
    Content encoder follows [20],[21]; the paper assumes these units carry no speaker or emotion information.
  • domain assumption The Wang et al. [23] diarization model provides frame-level emotion embeddings transferable to new speakers and emotions
    Temporal emotion encoder is used frozen; no fine-tuning or domain adaptation is described.
  • ad hoc to paper Prosody augmentation with random shifts and warps simulates real prosody mismatch
    This is the load-bearing premise of EEPT; no evidence connects the augmentation distribution to actual mismatch statistics.
  • domain assumption Reconstruction training with xc=xe=xs leads to three disentangled representations
    Standard assumption in EVC; the paper relies on GRL and losses to separate, but no formal identifiability is shown.
  • domain assumption Gradient reversal removes residual content and emotion from the target representation
    GRL from [18] is assumed to suppress information; effectiveness is only assessed indirectly via ablations.

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Cite this review

Pith. "Pith review of Maestro-EVC: Controllable Emotional Voice Conversion Guided by References and Explicit Prosody." pith.science (2026). https://pith.science/paper/PWPMUTIG

@misc{pith2026250806890,
  author       = {Pith},
  title        = {Pith review of: Maestro-EVC: Controllable Emotional Voice Conversion Guided by References and Explicit Prosody},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PWPMUTIG}},
  note         = {Machine review of arXiv:2508.06890}
}
read the original abstract

Emotional voice conversion (EVC) aims to modify the emotional style of speech while preserving its linguistic content. In practical EVC, controllability, the ability to independently control speaker identity and emotional style using distinct references, is crucial. However, existing methods often struggle to fully disentangle these attributes and lack the ability to model fine-grained emotional expressions such as temporal dynamics. We propose Maestro-EVC, a controllable EVC framework that enables independent control of content, speaker identity, and emotion by effectively disentangling each attribute from separate references. We further introduce a temporal emotion representation and an explicit prosody modeling with prosody augmentation to robustly capture and transfer the temporal dynamics of the target emotion, even under prosody-mismatched conditions. Experimental results confirm that Maestro-EVC achieves high-quality, controllable, and emotionally expressive speech synthesis.

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Forward citations

Cited by 2 Pith papers

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  1. The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation

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    Emotion embedding similarities are unsuitable for zero-shot evaluation of emotional expressiveness in speech generation due to confounding by non-emotional acoustic features.

  2. The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation

    eess.AS 2026-04 conditional novelty 6.0 of 10

    Emotion-embedding cosine similarity (EMO-SIM) fails to track emotion under speaker or wording changes and misaligns with human perception, so it is unreliable for evaluating expressive speech generation.

Reference graph

Works this paper leans on

36 extracted references · 21 canonical work pages · cited by 1 Pith paper

  1. [6]

    Pavits: Exploring prosody-aware vits for end-to-end emotional voice conversion,

    T. Qi, W. Zheng, C. Lu, Y . Zong, and H. Lian, “Pavits: Exploring prosody-aware vits for end-to-end emotional voice conversion,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 12 697–12 701

    work page 2024

  2. [1]

    Emotional voice conversion: Theory, databases and esd,

    K. Zhou, B. Sisman, R. Liu, and H. Li, “Emotional voice conversion: Theory, databases and esd,” Speech Communication, vol. 137, pp. 1–18, 2022

    work page 2022

  3. [2]

    Training socially engaging robots: Modeling backchannel behaviors with batch reinforce- ment learning,

    N. Hussain, E. Erzin, T. M. Sezgin, and Y . Yemez, “Training socially engaging robots: Modeling backchannel behaviors with batch reinforce- ment learning,” IEEE Transactions on Affective Computing , vol. 13, no. 4, pp. 1840–1853, 2022

    work page 2022

  4. [3]

    Real-time speech emotion analysis for smart home assistants,

    R. Chatterjee, S. Mazumdar, R. S. Sherratt, R. Halder, T. Maitra, and D. Giri, “Real-time speech emotion analysis for smart home assistants,” IEEE Transactions on Consumer Electronics , vol. 67, no. 1, pp. 68–76, 2021

    work page 2021

  5. [4]

    Pittermann, A

    J. Pittermann, A. Pittermann, and W. Minker, Handling emotions in human-computer dialogues. Springer, 2010

    work page 2010

  6. [5]

    Toward artificial emotional intelligence for cooperative social human–machine interaction,

    B. A. Erol, A. Majumdar, P. Benavidez, P. Rad, K.-K. R. Choo, and M. Jamshidi, “Toward artificial emotional intelligence for cooperative social human–machine interaction,” IEEE Transactions on Computa- tional Social Systems , vol. 7, no. 1, pp. 234–246, 2019

    work page 2019

  7. [7]

    Converting anyone’s emotion: Towards speaker-independent emotional voice conversion,

    K. Zhou, B. Sisman, M. Zhang, and H. Li, “Converting anyone’s emotion: Towards speaker-independent emotional voice conversion,” arXiv preprint arXiv:2005.07025 , 2020

    arXiv 2005

  8. [8]

    Limited Data Emotional Voice Conversion Leveraging Text-to-Speech: Two-stage Sequence-to-Sequence Training

    K. Zhou, B. Sisman, and H. Li, “Limited data emotional voice conversion leveraging text-to-speech: Two-stage sequence-to-sequence training,” arXiv preprint arXiv:2103.16809 , 2021

    work page Pith review arXiv 2021

Show all 36 references
  1. [9]

    Emotion inten- sity and its control for emotional voice conversion,

    K. Zhou, B. Sisman, R. Rana, B. W. Schuller, and H. Li, “Emotion inten- sity and its control for emotional voice conversion,” IEEE Transactions on Affective Computing , vol. 14, no. 1, pp. 31–48, 2022

    2022

  2. [10]

    Seen and unseen emotional style transfer for voice conversion with a new emotional speech dataset,

    K. Zhou, B. Sisman, R. Liu, and H. Li, “Seen and unseen emotional style transfer for voice conversion with a new emotional speech dataset,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 920–924

    2021

  3. [11]

    Emotional voice conversion with semi-supervised generative modeling,

    H. Zhu, H. Zhan, H. Cheng, and Y . Wu, “Emotional voice conversion with semi-supervised generative modeling,” in Ann. Conf. Int. Speech Commun. Assoc.(INTERSPEECH), 2023

    2023

  4. [12]

    Speaker-independent emotional voice conversion via disentangled rep- resentations,

    X. Chen, X. Xu, J. Chen, Z. Zhang, T. Takiguchi, and E. R. Hancock, “Speaker-independent emotional voice conversion via disentangled rep- resentations,” IEEE Transactions on Multimedia, vol. 25, pp. 7480–7493, 2022

    2022

  5. [13]

    Nonparallel emotional voice conversion for unseen speaker-emotion pairs using dual domain adversarial network & virtual domain pairing,

    N. Shah, M. Singh, N. Takahashi, and N. Onoe, “Nonparallel emotional voice conversion for unseen speaker-emotion pairs using dual domain adversarial network & virtual domain pairing,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Pro- cessin...

    2023

  6. [14]

    Enhancing zero- shot emotional voice conversion via speaker adaptation and duration prediction,

    S. Wang, T. Qi, C. Lu, Z. Luo, and W. Zheng, “Enhancing zero- shot emotional voice conversion via speaker adaptation and duration prediction,” in ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2025, pp. 1–5

    2025

  7. [15]

    Zero shot audio to audio emotion trans- fer with speaker disentanglement,

    S. Dutta and S. Ganapathy, “Zero shot audio to audio emotion trans- fer with speaker disentanglement,” in ICASSP 2024-2024 IEEE In- ternational Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024, pp. 10 371–10 375

    2024

  8. [16]

    Multi-speaker emotional speech synthesis with fine-grained prosody modeling,

    C. Lu, X. Wen, R. Liu, and X. Chen, “Multi-speaker emotional speech synthesis with fine-grained prosody modeling,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Pro- cessing (ICASSP). IEEE, 2021, pp. 5729–5733

    2021

  9. [17]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017

    2017

  10. [18]

    Unsupervised domain adaptation by back- propagation,

    Y . Ganin and V . Lempitsky, “Unsupervised domain adaptation by back- propagation,” in International conference on machine learning. PMLR, 2015, pp. 1180–1189

    2015

  11. [19]

    Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,

    J. Kong, J. Kim, and J. Bae, “Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,” Advances in neural information processing systems , vol. 33, pp. 17 022–17 033, 2020

    2020

  12. [20]

    Sef-vc: Speaker embedding free zero-shot voice conversion with cross attention,

    J. Li, Y . Guo, X. Chen, and K. Yu, “Sef-vc: Speaker embedding free zero-shot voice conversion with cross attention,” in ICASSP 2024- 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024, pp. 12 296–12 300

    2024

  13. [21]

    Textless speech emotion conversion using discrete and decomposed representations,

    F. Kreuk, A. Polyak, J. Copet, E. Kharitonov, T.-A. Nguyen, M. Rivi `ere, W.-N. Hsu, A. Mohamed, E. Dupoux, and Y . Adi, “Textless speech emotion conversion using discrete and decomposed representations,” arXiv preprint arXiv:2111.07402 , 2021

    2021 arXiv

  14. [22]

    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 transactions on audio, speech, and language processing , vol. 29, pp. 3451–3460, 2021

    2021

  15. [23]

    Speech emotion diarization: Which emotion appears when?

    Y . Wang, M. Ravanelli, and A. Yacoubi, “Speech emotion diarization: Which emotion appears when?” in 2023 IEEE Automatic Speech Recog- nition and Understanding Workshop (ASRU) . IEEE, 2023, pp. 1–7

    2023

  16. [24]

    Smoothing and differentiation of data by simplified least squares procedures

    A. Savitzky and M. J. Golay, “Smoothing and differentiation of data by simplified least squares procedures.” Analytical chemistry , vol. 36, no. 8, pp. 1627–1639, 1964

    1964

  17. [25]

    Disentanglement of emotional style and speaker identity for expressive voice conversion,

    Z. Du, B. Sisman, K. Zhou, and H. Li, “Disentanglement of emotional style and speaker identity for expressive voice conversion,” arXiv preprint arXiv:2110.10326, 2021

    2021 arXiv

  18. [26]

    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,” arXiv preprint arXiv:2005.07143 , 2020

    2005 arXiv

  19. [27]

    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,” in2015 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2015, pp. 5206–5210

    2015

  20. [28]

    V oxceleb: a large-scale speaker identification dataset,

    A. Nagrani, J. S. Chung, and A. Zisserman, “V oxceleb: a large-scale speaker identification dataset,” arXiv preprint arXiv:1706.08612 , 2017

    2017 arXiv

  21. [29]

    World: a vocoder-based high-quality speech synthesis system for real-time applications,

    M. Morise, F. Yokomori, and K. Ozawa, “World: a vocoder-based high-quality speech synthesis system for real-time applications,” IEICE TRANSACTIONS on Information and Systems , vol. 99, no. 7, pp. 1877– 1884, 2016

    2016

  22. [30]

    CSTR VCTK Corpus: English multi-speaker corpus for CSTR voice cloning toolkit,

    J. Yamagishi, C. Veaux, and K. MacDonald, “CSTR VCTK Corpus: English multi-speaker corpus for CSTR voice cloning toolkit,” 2017

    2017

  23. [31]

    Crema-d: Crowd-sourced emotional multimodal actors dataset,

    H. Cao, D. G. Cooper, M. K. Keutmann, R. C. Gur, A. Nenkova, and R. Verma, “Crema-d: Crowd-sourced emotional multimodal actors dataset,” IEEE transactions on affective computing , vol. 5, no. 4, pp. 377–390, 2014

    2014

  24. [32]

    Iemocap: Interactive emotional dyadic motion capture database,

    C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. N. Chang, S. Lee, and S. S. Narayanan, “Iemocap: Interactive emotional dyadic motion capture database,” Language resources and evaluation , vol. 42, no. 4, pp. 335–359, 2008

    2008

  25. [33]

    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,” in International conference on machine learning . PMLR, 2023, pp. 28 492–28 518

    2023

  26. [34]

    emotion2vec: Self-supervised pre-training for speech emotion repre- sentation,

    Z. Ma, Z. Zheng, J. Ye, J. Li, Z. Gao, S. Zhang, and X. Chen, “emotion2vec: Self-supervised pre-training for speech emotion repre- sentation,” arXiv preprint arXiv:2312.15185 , 2023

    2023 arXiv

  27. [35]

    Pearson correlation coefficient,

    I. Cohen, Y . Huang, J. Chen, J. Benesty, J. Benesty, J. Chen, Y . Huang, and I. Cohen, “Pearson correlation coefficient,” Noise reduction in speech processing, pp. 1–4, 2009

    2009

  28. [36]

    Dynamic time warping,

    M. M ¨uller, “Dynamic time warping,” Information retrieval for music and motion, pp. 69–84, 2007

    2007

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