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REVIEW 3 major objections 4 minor 60 references

Multi-interaction TTS toward professional recording reproduction

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Text-to-speech accepts follow-up directions to refine style.

desk verdict Genuinely new multi-turn TTS refinement task with honest reporting, but the evidence is suggestive, not conclusive, and the domain-gap risk deserves scrutiny. read the letter →

arxiv 2507.00808 v2 pith:IHCXOTR2 submitted 2025-07-01 cs.SD cs.CLeess.AS

Hiroki Kanagawa , Kenichi Fujita , Aya Watanabe , Yusuke Ijima This is my paper
classification cs.SDcs.CLeess.AS
keywords text-to-speechexpressivespeechsynthesisspeakingstylerefinementiterativeinteractionembeddingvoicedirectiondatasettext-promptedTTSmulti-step
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 proposes a text-to-speech system that treats the synthesizer as a voice actor and lets a user act as a voice director, issuing several written directions in sequence. The authors build a new dataset that records actors responding to two or three staged direction rounds, then train a style refiner that moves the speech embedding of the previous output toward the requested direction. Subjective tests show that iterating directions moves the synthesized style in the requested direction, that this tracks the oracle condition which uses real recordings, and that naturalness is not degraded. If the claim holds, expressive TTS moves from re-prompting from scratch to a loop where the previous synthesis itself becomes the input to the next refinement.

What carries the argument

The load-bearing mechanism is the style refiner trained separately from the backbone TTS model. The backbone is conditioned on an embedding from a self-supervised speech encoder and a style-token layer; the refiner takes the intermediate frame-level representation $r$, attends to the direction text, aggregates the result, and applies a FiLM conditioning layer to convert the current embedding $x$ into the refined embedding $x'$. Training uses L1 regression on embeddings extracted from recordings: the input is the pre-refined take, the target is the actor's refined take, and the direction text is the conditioning. At inference this learned map is applied repeatedly, feeding each synthesized utterance back as the next iteration's input.

What would settle it

Starting from a recorded take, apply the style refiner, take the synthetic output, apply a second direction, and measure whether each step moves the embedding toward the corresponding refined recorded take and whether the subjective directional score holds. If the second-step movement shrinks sharply or the score degrades compared with the first step, the recorded-to-synthetic transfer assumption breaks and the iterative loop would not survive multiple rounds.

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Extended reading notes

Core claim

The central discovery is that iterative style refinement can be implemented by manipulating a single global speech embedding, with no change to the linguistic content. Given a pre-refined embedding $x$ and a direction text, a style refiner predicts a refined embedding $x'$, which the backbone TTS model renders as speech; at the next round that synthetic speech supplies the new pre-refined embedding. Trained on a dataset of direction cycles, recorded actor performances with two or three textually generated directions per manuscript, the refiner yields synthesized speech whose style follows the directions, although scores remain below the oracle condition in which the real actor take is used. Naturalness ratings show no significant difference among single-shot, iterative, and oracle conditions. The authors frame the result as a first step: directions that target specific words or linguistic features such as pauses are not yet realized, because a global embedding cannot alter individual positions in a sentence.

Load-bearing premise

The style refiner is trained on recordings by real actors, but at use it is applied to the TTS system's own synthetic speech; the method assumes synthetic speech sits close enough to the recorded training examples that the same refinement still works across multiple rounds.

Editorial extensions

If this is right

  • Refinement becomes a sequence: a user can issue a direction, hear the result, and then issue another direction, with each new synthesis feeding the next round.
  • Iterative direction-following does not cost naturalness: subjective naturalness was statistically indistinguishable between single-shot, iterative, and oracle-generated speech.
  • The method handles complex and abstract directions beyond simple high/low or fast/slow prompts, because the direction texts were created to mimic real voice-directing language.
  • Style-group breakdowns show the effect across fear, joy, anger, sadness, and surprise, with joy and surprise matching the oracle statistically in refinement score.
  • Directions that name a particular word, phrase, or pause location are explicitly outside the reach of a global-embedding approach, pointing toward fine-grained TTS as the next step.

Reading between the lines

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

  • Beyond the paper: the practical ceiling is set by the unseen loop condition; the refiner is trained on recorded pre- and post-refinement embeddings but deployed on the TTS model's own synthetic embeddings, so multi-round performance depends on how far synthetic embeddings drift from recorded ones.
  • A direct stress test would be to train on pairs where both sides are synthetic, or to add an embedding-space alignment loss, and then compare iterative refinement quality; the paper does not report such a comparison.
  • Because the refiner is speaker-dependent, the natural next boundary is cross-speaker generalization; without speaker conditioning or retraining, the interaction loop may not transfer to unseen voices.
  • The dataset ties each direction cycle to a style label, so it could be repurposed for automatic evaluation, for example by measuring whether the embedding trajectory across sessions moves toward the embedding of the labeled target style.
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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 / 4 minor

Summary. The paper proposes a multi-interaction TTS framework in which a style refiner takes the speech embedding extracted from the current synthesis plus a textual direction, predicts a refined embedding, and feeds it back through a style-token layer into a FastSpeech2-based backbone TTS. To support training, the authors collected a Japanese dataset of 1,606 direction cycles with two voice actors, 52 speaking styles, and LLM-generated sequences of two or three iterative directions, and augmented the direction texts with LLM-based paraphrases. Three crowdsourced subjective evaluations are reported: an iterative style-refinement MOS test comparing the proposed iterative condition with an actor-guided oracle and an identical-speech baseline, a style-refinement accuracy test using matched versus random directions, and a naturalness MOS test. The paper concludes that the method achieves iterative style refinement that follows user directions 'to some extent' without degrading naturalness, and it candidly discusses remaining failures for position-specific and linguistic instructions.

Significance. If the central claim holds, this is a useful step toward interactive TTS: the dataset of direction cycles is a potential resource for the community, the style-refiner design is simple and modular, and the idea of modeling the voice-director/voice-actor loop is well motivated. The authors deserve credit for including an oracle condition, a random-direction control, confidence intervals, significance testing, and a publicly available demo page, as well as for explicitly acknowledging in Sect. 5 which direction types are not captured. However, the observed effect is modest: the overall iterative score (2.90) is below the 'Overall alignment only' anchor of 3 and well below the oracle (3.27), and the evidence for the multi-step claim rests on a small number of tested cycles and on an unquantified assumption that a refiner trained on recorded-speech embeddings transfers to synthetic-speech embeddings inside an iterative loop.

major comments (3)
  1. [Section 2.3 / Section 4.2] The style refiner is trained exclusively on embeddings extracted from recorded speech (x from the pre-refined take, x' from the refined take), yet at inference, including in the Iterative condition of Table 3, it is applied to embeddings extracted from synthetic speech (Identical-0, Iterative-N). The paper does not quantify the distribution shift between recorded and synthetic embeddings, nor does it include any domain-adaptation or correction. This is load-bearing for the central 'multi-interaction' claim: if the synthetic embeddings lie outside the training distribution, the L1 regression may rely on spurious recorded/synthetic differences, and errors could accumulate over iterations. The subjective test in Sect. 4.2 partially exercises the shift, but with only 16 direction cycles total. Please add a quantitative analysis of the recorded-to-synthetic embedding gap (for example, centroid distances or nearest-neighbor overlap for matched content and style), and/or report iterative refinement results on a substantially larger set of cycles, including session-by-session scores.
  2. [Section 4.2] The only non-oracle baseline in the iterative-refinement evaluation is Identical (no refinement), so the significant improvement over Identical demonstrates that the refiner changes something, not that the change follows the direction. The absolute scores are not strong: the overall Iterative mean is 2.90, which is below the 'Overall alignment only' anchor of 3, and the Actor-Guided oracle itself reaches only 3.27. To support the abstract's claim that refinement is 'in accordance with users' directions', please add a baseline that receives the same direction text without the speech prompt (e.g., PromptTTS-style conditioning) or a single-step version of the proposed refiner, and report effect sizes and direction-type-wise results. As it stands, the comparison to Identical is a minimal sanity check rather than a demonstration that the iterative loop is the source of the improvement.
  3. [Section 4.3 / Table 4] The style-refinement accuracy experiment is conducted under the Single-shot condition of Table 2, where the pre-refined and refined inputs are recorded speech or synthetic speech generated directly from recorded prompts; it therefore does not measure behavior inside the iterative loop, which is the central new claim. The paper also states that Random (Similar) 'yield comparable performance to Matched' without reporting a significance test for that pair. In addition, Table 4 and Sect. 5.1 show that position-specific and linguistic directions are not handled by the current global-embedding refiner. The abstract and conclusion should either explicitly restrict the claim to global paralinguistic style directions or report results separately by direction category; as written, the claim is broader than the evidence.
minor comments (4)
  1. [Section 2.2] The sentence 'STL post-process x was applied' appears to be missing a verb form and should read 'STL post-processes x'.
  2. [Section 3.2] The statement 'As the style refiner, we utilized Gemma2 LLM fine-tuned on a Japanese dataset' is inconsistent with the architecture in Sect. 2.3, which describes cross-attention, an aggregation module, and FiLM. Please clarify whether Gemma2 is used only as the direction-text encoder or is the full refiner.
  3. [Figures 4 and 6] The 'n.s.' markers in Figures 4 and 6 are not tied to specific pairs; please state which comparisons are not significant (for example, Iterative versus Actor-Guided within the Joy and Surprise groups, or the cycle-by-cycle comparisons).
  4. [Section 4.3] The split of Random into Similar and Dissimilar relies on ChatGPT o3-mini similarity scores with a threshold, but no reliability or agreement measure is reported for this automatic classification; a brief validation would strengthen the interpretation.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: style refiner is trained on external recorded embeddings and evaluated on held-out subjective trials.

full rationale

No circular step found. The style refiner (Sect. 2.3) is trained with an L1 loss against x' embeddings extracted from actually recorded, director-refined actor speech, and the refinement evaluations use direction cycles held out from training (Sect. 3.1: 104 cycles for validation and 104 for test; Sect. 4.2: 16 direction cycles per method; Sect. 4.3: 50 sessions). The central claim—that a direction text moves the embedding so that the TTS output aligns with the direction—is tested against subjective judgments with Matched vs. Random controls, not against the training target itself. The use of the same author group's SSL encoder [27] and style-grouping protocol [42] is a self-citation, but neither is load-bearing in the sense of smuggling the conclusion: [27] merely supplies a pretrained HuBERT feature extractor, and [42] defines a grouping of the 52 style labels for display. No equation or fitted constant is renamed as a prediction; no result reduces to its input by construction. The recorded-to-synthetic embedding shift flagged as a possible weakness is a distribution-shift correctness risk, not a form of circularity.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central method adds no new physical or mathematical entities. It relies on four domain assumptions: LLM-generated directions approximate real direction behavior; a global speech embedding is sufficient to realize the tested directions; L1 embedding regression transfers from recorded to synthetic speech; and the subjective MOS protocol measures direction alignment. The only hand-chosen numeric that directly shapes a reported comparison is the cutoff separating Random-Similar from Random-Dissimilar in Section 4.3.

free parameters (1)
  • Random condition similarity cutoff = ChatGPT o3-mini similarity score 3 or higher = Similar; 2 or lower = Dissimilar
    Ad hoc split of the Random condition in Section 4.3 using ChatGPT o3-mini after data collection; the threshold directly shapes the claim that Matched outperforms Random(Dissimilar) while Random(Similar) approximates Matched.
assumptions (4)
  • domain assumption LLM-generated direction texts are representative of real voice-director instructions.
    Section 2.1 states directions were generated by ChatGPT-4o without referring to actual recordings; the paper assumes this preserves direction quality and diversity.
  • domain assumption A global speech embedding captures enough style information to realize the tested directions.
    The style refiner modifies only a global embedding; Section 5.1 admits that position-specific instructions such as 'at the beginning' cannot be handled by this representation.
  • domain assumption L1 regression in embedding space trained on recorded speech transfers to synthetic speech at inference.
    Section 2.3 trains the refiner on embeddings from recorded actor speech, while the iterative evaluation feeds it embeddings from the TTS model's own synthetic output.
  • domain assumption The subjective MOS protocol with a direction text is a valid measure of style refinement.
    Section 4.2 uses listener ratings of whether output speech reflects the direction; the paper itself discusses in Section 5.3 that this coarse evaluation may miss subtle stylistic differences.

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Cite this review

Pith. "Pith review of Multi-interaction TTS toward professional recording reproduction." pith.science (2026). https://pith.science/paper/IHCXOTR2

@misc{pith2026250700808,
  author       = {Pith},
  title        = {Pith review of: Multi-interaction TTS toward professional recording reproduction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IHCXOTR2}},
  note         = {Machine review of arXiv:2507.00808}
}
read the original abstract

Voice directors often iteratively refine voice actors' performances by providing feedback to achieve the desired outcome. While this iterative feedback-based refinement process is important in actual recordings, it has been overlooked in text-to-speech synthesis (TTS). As a result, fine-grained style refinement after the initial synthesis is not possible, even though the synthesized speech often deviates from the user's intended style. To address this issue, we propose a TTS method with multi-step interaction that allows users to intuitively and rapidly refine synthesized speech. Our approach models the interaction between the TTS model and its user to emulate the relationship between voice actors and voice directors. Experiments show that the proposed model with its corresponding dataset enables iterative style refinements in accordance with users' directions, thus demonstrating its multi-interaction capability. Sample audios are available: https://ntt-hilab-gensp.github.io/ssw13multiinteractiontts/

Figures

Figures reproduced from arXiv: 2507.00808 by the authors.

Figure 1
Figure 1. Overview of the task and our proposed method. users to iteratively select speech prompts or text prompts until the desired synthetic style is achieved. To enhance the applicability of speech synthesis by en￾abling the easy generation of speech with the desired style, we believe incorporating iterative refinement is promising. Build￾ing on this idea, we propose a new expressive TTS method guided by multiple direction… view at source ↗
Figure 2
Figure 2. Overview of the direction cycle in dataset collection [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Overview of the proposed model. 2.3. Style refiner The style refiner refines x, the pre-refined embedding, into x ′ , the refined embedding, using information from the direction text. The style refiner consists of three components: cross￾attention, aggregation module, and FiLM [30]. Cross attention integrates information from speech and text, as in other stud￾ies on text-speech integration [31, 32]. To handle richer… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Subjective evaluation results for iterative style refine￾ment. (a) compares each method regarding direction-cycle. (b) breaks them down into five style groups and shows the overall results. ative (ours) implies that there is still a discrepancy from the desired style. …
Figure 6
Figure 6. Figure 6: Subjective evaluation results for naturalness. Similar to [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

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Works this paper leans on

60 extracted references · 52 canonical work pages

  1. [1]

    Multi-interaction TTS toward professional recording reproduction

    Introduction Almost all creative activities entail a lot of trial and error to pro- duce high-quality work [1–3]. Film or television directors often require multiple takes, while photographers frequently adjust angles and camera settings. Similarly, voice directors often iter- atively refine voice actors’ performance by providing feedback to achieve the d...

    work page Pith review arXiv 2025

  2. [2]

    During the recording of this dataset, a di- rector iteratively gave acting directions, and the voice actor then reflected the given directions

    Proposed method To model the interaction between the voice director and the voice actor, we constructed a dataset that imitates an actual recording by them. During the recording of this dataset, a di- rector iteratively gave acting directions, and the voice actor then reflected the given directions. To model this direction cycle, we constructed a model ca...

    work page

  3. [3]

    Speak more brightly,

    Instructions for modifying para-linguistic and non-linguistic information in the acted speech (e.g., “Speak more brightly,” “Be more considerate of the other person’s feelings”)

    work page

  4. [4]

    In- sert a silent-pause after this word,

    Instructions for modifying linguistic information (e.g., “In- sert a silent-pause after this word,” “Change the location of stress (or accent (for pitch-accent language, i.e., Japanese))”)

    work page

  5. [5]

    Follow my example

    Demonstrative instructions, where the director provides a performance example (e.g., “Follow my example”)

    work page

  6. [6]

    The Guideline for TTS Speaking Style Classification

    Instructions involving gestures and body language. While it would be ideal to construct a dataset that contains all of these categories, creating such a comprehensive dataset is highly complex and presents substantial challenges. Therefore, in this study, we constructed a dataset limited to categories 1 and 2, which can be expressed solely in textual form...

    work page

  7. [7]

    Dataset We used three Japanese 22 kHz datasets: interactive, non- interactive, and large in-house datasets

    Experimental setup 3.1. Dataset We used three Japanese 22 kHz datasets: interactive, non- interactive, and large in-house datasets. The interactive and non-interactive datasets are from the same two voice actors (one female and one male). The interactive dataset was specially designed for this study as described in Sect. 2.1. It includes 13.7 hours of Jap...

    work page

  8. [8]

    The backbone TTS model was trained in 600K steps using the large in-house dataset

    work page

Show all 60 references
  1. [9]

    The backbone TTS model was further trained with GAN in the same manner as [39,40] for an additional 200K steps with a fixed learning rate of 1 × 10−4 to improve the naturalness of the synthesized speech

  2. [10]

    The loss function and learning rate were the same as in the previous step

    The model was then fine-tuned in 30K steps with the interac- tive and the non-interactive datasets to improve the speaking style reproduction for each voice actor. The loss function and learning rate were the same as in the previous step

  3. [11]

    The training step was 10K steps with AdamW optimizer [41] with 4K warm-up steps

    The style refiner was trained by the interactive dataset. The training step was 10K steps with AdamW optimizer [41] with 4K warm-up steps

  4. [12]

    Overall alignment only

    Subjective evaluation We conducted three subjective evaluations to confirm the ef- fectiveness of the proposed method. The evaluations examined 2https://huggingface.co/tokyotech-llm/Llama-3. 1-Swallow-8B-Instruct-v0.2 3https://huggingface.co/rinna/japanese-hubert-base 4https:/...

  5. [13]

    at the beginning

    Discussion As described in Sect. 2.1, our dataset includes difficult direc- tions that were not included in previous text-prompt-based TTS methods. We believe that this explains why the overall score in Sect. 4.2 is relatively low; it remains around three even in non-iterative...

  6. [14]

    Subjective evaluations demonstrated that our proposed method achieved iterative style refinement that matched the user’s di- rections to some extent

    Conclusion In this paper, we proposed a method for TTS systems that can model the interaction between voice directors and voice actors. Subjective evaluations demonstrated that our proposed method achieved iterative style refinement that matched the user’s di- rections to some...

  7. [15]

    The relevance of trial-and-error: Can trial-and- error be a sufficient learning method in technical problem-solving- contexts?

    K.-C. T ¨onnsen, “The relevance of trial-and-error: Can trial-and- error be a sufficient learning method in technical problem-solving- contexts?” Techne Series - Research in Sloyd Education and Craft Science A, vol. 28, no. 2, p. 303–312, Apr. 2021

    2021

  8. [16]

    From da Vinci’s flying machines to a theory of the creative process,

    C. D. G ¨uss, S. Ahmed, and D. D ¨orner, “From da Vinci’s flying machines to a theory of the creative process,”Perspectives on Psy- chological Science, vol. 16, no. 6, pp. 1184–1197, 2021

    2021

  9. [17]

    What are the stages of the creative process? What visual art students are saying

    M. Botella, F. Zenasni, and T. Lubart, “What are the stages of the creative process? What visual art students are saying.” Frontiers in Psychology, vol. 9, 2018

    2018

  10. [18]

    From page to stage: The director’s interpretation and picturization of a script,

    N. J. Glikpoe and I. Horsu, “From page to stage: The director’s interpretation and picturization of a script,” Advanced Journal of Theatre and Film Studies, vol. 1, no. 1, pp. 36–42, 2023

    2023

  11. [19]

    Showing and telling—How directors combine embodied demonstrations and verbal descrip- tions to instruct in theater rehearsals,

    A. Schmidt and A. Deppermann, “Showing and telling—How directors combine embodied demonstrations and verbal descrip- tions to instruct in theater rehearsals,” Frontiers in Communica- tion, vol. 7, p. 955583, 2023

    2023

  12. [20]

    Hi- erarchical text-conditional image generation with CLIP latents,

    A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hi- erarchical text-conditional image generation with CLIP latents,” arXiv, 2022, arXiv:2204.06125

    2022 arXiv

  13. [21]

    High-resolution image synthesis with latent diffusion models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” Proc. CVPR, pp. 10 684–10 695, 2022

    2022

  14. [22]

    Photorealistic text-to- image diffusion models with deep language understanding,

    C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S. K. S. Ghasemipour, R. Gontijo-Lopes, B. K. Ayan, T. Sali- mans, J. Ho, D. J. Fleet, and M. Norouzi, “Photorealistic text-to- image diffusion models with deep language understanding,”Proc. NeurIPS, vol. 35, pp. 36 4...

    2022

  15. [23]

    Program synthesis with large language models,

    J. Austin, A. Odena, M. I. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. J. Cai, M. Terry, Q. V . Le, and C. Sutton, “Program synthesis with large language models,” arXiv, 2021, arXiv:2108.07732

    2021 arXiv

  16. [24]

    CodeGen: An open large language model for code with multi-turn program synthesis,

    E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y . Zhou, S. Savarese, and C. Xiong, “CodeGen: An open large language model for code with multi-turn program synthesis,” Proc. ICLR, pp. 284–295, 2023

    2023

  17. [25]

    Training language models to follow instruc- tions with human feedback,

    L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin et al., “Training language models to follow instruc- tions with human feedback,” Proc. NeurIPS, vol. 35, pp. 27 730– 27 744, 2022

    2022

  18. [26]

    PaLM: Scaling language modeling with path- ways,

    A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts et al., “PaLM: Scaling language modeling with path- ways,” JMLR, vol. 24, no. 240, 2023

    2023

  19. [27]

    A survey on neural speech synthesis,

    X. Tan, T. Qin, F. Soong, and T.-Y . Liu, “A survey on neural speech synthesis,” arXiv, 2021, arXiv:2106.15561

    2021 arXiv

  20. [28]

    A review of deep learning techniques for speech processing,

    A. Mehrish, N. Majumder, R. Bharadwaj, R. Mihalcea, and S. Po- ria, “A review of deep learning techniques for speech processing,” Information Fusion, vol. 99, p. 101869, 2023

    2023

  21. [29]

    Model archi- tectures to extrapolate emotional expressions in DNN-based text- to-speech,

    K. Inoue, S. Hara, M. Abe, N. Hojo, and Y . Ijima, “Model archi- tectures to extrapolate emotional expressions in DNN-based text- to-speech,” Speech Communication, vol. 126, pp. 35–43, 2021

    2021

  22. [30]

    V oice puppetry: Exploring dramatic performance to develop speech synthesis,

    M. Aylett, D. Braude, C. Pidcock, and B. Potard, “V oice puppetry: Exploring dramatic performance to develop speech synthesis,” in Proc. 10th ISCA Workshop on Speech Synthesis (SSW 10) , 2019, pp. 117–120

    2019

  23. [31]

    V oice puppetry with FastPitch,

    E. Van De Vreken, K. Richmond, and C. Lai, “V oice puppetry with FastPitch,” in Proc. Interspeech, 2022, pp. 5219–5220

    2022

  24. [32]

    Style Tokens: Unsu- pervised style modeling, control and transfer in end-to-end speech synthesis,

    Y . Wang, D. Stanton, Y . Zhang, R.-S. Ryan, E. Battenberg, J. Shor, Y . Xiao, Y . Jia, F. Ren, and R. A. Saurous, “Style Tokens: Unsu- pervised style modeling, control and transfer in end-to-end speech synthesis,” Proc. ICML, vol. 80, pp. 5180–5189, 2018

    2018

  25. [33]

    Robust and fine-grained prosody control of end-to-end speech synthesis,

    Y . Lee and T. Kim, “Robust and fine-grained prosody control of end-to-end speech synthesis,” Proc. ICASSP, pp. 5911–5915, 2019

    2019

  26. [34]

    Fine- grained robust prosody transfer for single-speaker neural text-to- speech,

    V . Klimkov, S. Ronanki, J. Rohnke, and T. Drugman, “Fine- grained robust prosody transfer for single-speaker neural text-to- speech,” Proc. Interspeech, pp. 4440–4444, 2019

    2019

  27. [35]

    Daft- Exprt: Cross-speaker prosody transfer on any text for expressive speech synthesis,

    J. Zadi, H. Seut, B. van Niekerk, and M.-A. Carbonneau, “Daft- Exprt: Cross-speaker prosody transfer on any text for expressive speech synthesis,” Proc. Interspeech, pp. 4591–4595, 2022

    2022

  28. [36]

    PromptTTS: Controllable text-to-speech with text descriptions,

    Z. Guo, Y . Leng, Y . Wu, S. Zhao, and X. Tan, “PromptTTS: Controllable text-to-speech with text descriptions,”Proc. ICASSP, 2023

    2023

  29. [37]

    Natural language guidance of high- fidelity text-to-speech with synthetic annotations,

    D. Lyth and S. King, “Natural language guidance of high- fidelity text-to-speech with synthetic annotations,” arXiv, 2024, arXiv:2402.01912

    2024 arXiv

  30. [38]

    VoiceCraft: Zero-shot speech editing and text-to-speech in the wild,

    P. Peng, P.-Y . Huang, S.-W. Li, A. Mohamed, and D. Harwath, “VoiceCraft: Zero-shot speech editing and text-to-speech in the wild,” Proc. ACL, pp. 12 442–12 462, 2024

    2024

  31. [39]

    V oice at- tribute editing with text prompt,

    Z.-Y . Sheng, L.-J. Liu, Y . Ai, J. Pan, and Z.-H. Ling, “V oice at- tribute editing with text prompt,” TASLP, pp. 1–12, 2025

    2025

  32. [40]

    The guidelines for TTS speaking style classifi- cation (IT-4012),

    Japan Electronics and Information Technology Industries As- sociation, “The guidelines for TTS speaking style classifi- cation (IT-4012),” 2021, (In Japanese). [Online]. Available: https://www.jeita-speech.org/standard/standard 4012.html

    2021

  33. [41]

    Zero-shot text-to-speech synthesis conditioned using self- supervised speech representation model,

    K. Fujita, T. Ashihara, H. Kanagawa, T. Moriya, and Y . Ijima, “Zero-shot text-to-speech synthesis conditioned using self- supervised speech representation model,” in Proc. ICASSP Work- shops (ICASSPW), 2023

    2023

  34. [42]

    Why does self-supervised learning for speech recognition benefit speaker recognition?

    S. Chen, Y . Wu, C. Wang, S. Liu, Z. Chen, P. Wang et al. , “Why does self-supervised learning for speech recognition benefit speaker recognition?” in Proc. Interspeech, 2022, pp. 3699–3703

    2022

  35. [43]

    Feed-forward networks with atten- tion can solve some long-term memory problems,

    C. Raffel and D. P. W. Ellis, “Feed-forward networks with atten- tion can solve some long-term memory problems,” in Proc. ICLR Workshop, 2016

    2016

  36. [44]

    FiLM: Visual reasoning with a general conditioning layer,

    E. Perez, F. Strub, H. de Vries, V . Dumoulin, and A. Courville, “FiLM: Visual reasoning with a general conditioning layer,” in Proc. AAAI, vol. 32, no. 1, 2018

    2018

  37. [45]

    Learning alignment for multimodal emotion recognition from speech,

    H. Xu, H. Zhang, K. Han, Y . Wang, Y . Peng, and X. Li, “Learning alignment for multimodal emotion recognition from speech,” in Proc. Interspeech, 2019, pp. 3569–3573

    2019

  38. [46]

    Multimodal cross- and self-attention network for speech emotion recognition,

    L. Sun, B. Liu, J. Tao, and Z. Lian, “Multimodal cross- and self-attention network for speech emotion recognition,” in Proc. ICASSP, 2021, pp. 4275–4279

    2021

  39. [47]

    Hello GPT-4o,

    OpenAI, “Hello GPT-4o,” 2024, accessed:2024-02-14

    2024

  40. [48]

    Rephrasing the web: A recipe for compute and data-efficient lan- guage modeling,

    P. Maini, S. Seto, H. Bai, D. Grangier, Y . Zhang, and N. Jaitly, “Rephrasing the web: A recipe for compute and data-efficient lan- guage modeling,” in Proc. ICLR Workshop, 2024

    2024

  41. [49]

    FastSpeech 2: Fast and high-quality end-to-end text to speech,

    Y . Ren, C. Hu, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y . Liu, “FastSpeech 2: Fast and high-quality end-to-end text to speech,” in Proc. ICLR, 2020

    2020

  42. [50]

    In- vestigating on incorporating pretrained and learnable speaker rep- resentations for multi-speaker multi-style text-to-speech,

    C.-M. Chien, J.-H. Lin, C.-y. Huang, P.-c. Hsu, and H.-y. Lee, “In- vestigating on incorporating pretrained and learnable speaker rep- resentations for multi-speaker multi-style text-to-speech,” inProc. ICASSP, 2021, pp. 8588–8592

    2021

  43. [51]

    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,” inProc. NeurIPS, vol. 33, 2020, pp. 17 022–17 033

    2020

  44. [52]

    The curse of recursion: Training on generated data makes models forget,

    I. Shumailov, Z. Shumaylov, Y . Zhao, Y . Gal, N. Papernot, and R. Anderson, “The curse of recursion: Training on generated data makes models forget,” arXiv, 2024, arXiv:2305.17493

    2024 arXiv

  45. [53]

    Multi-speaker modeling for DNN- based speech synthesis incorporating generative adversarial net- works,

    H. Kanagawa and Y . Ijima, “Multi-speaker modeling for DNN- based speech synthesis incorporating generative adversarial net- works,” in Proc. SSW, 2019, pp. 40–44

    2019

  46. [54]

    Variational discriminator bottleneck: Improving imitation learn- ing, inverse RL, and GANs by constraining information flow,

    X. B. Peng, A. Kanazawa, S. Toyer, P. Abbeel, and S. Levine, “Variational discriminator bottleneck: Improving imitation learn- ing, inverse RL, and GANs by constraining information flow,” in Proc. ICLR, 2019

    2019

  47. [55]

    Decoupled weight decay regulariza- tion,

    I. Loshchilov and F. Hutter, “Decoupled weight decay regulariza- tion,” in Proc. ICLR, 2019

    2019

  48. [56]

    Expressive text-to-speech synthesis using text chat dataset with speaking style information,

    Y . Homma, H. Kanagawa, N. Kobayashi, Y . Ijima, and K. Saito, “Expressive text-to-speech synthesis using text chat dataset with speaking style information,” Transactions of JSAI, vol. 38, no. 3, pp. F–MA7, May 2023, (In Japanese)

    2023

  49. [57]

    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, Z. Wu, T. Qin, X.-Y . Li, W. Ye, S. Zhang, J. Bian, L. He, J. Li, and S. Zhao, “NaturalSpeech 3: Zero-shot speech synthesis with factorized codec and diffusion models,” in Proc. ICML, 2024

    2024

  50. [58]

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

    S. Chen, C. Wang, Y . Wu, Z. Zhang, L. Zhou, S. Liu, Z. Chen, Y . Liu, H. Wang, J. Li, L. He, S. Zhao, and F. Wei, “Neural codec language models are zero-shot text to speech synthesizers,” TASLP, vol. 33, pp. 705–718, 2025

    2025

  51. [59]

    FETV: A benchmark for fine-grained evaluation of open-domain text-to-video generation,

    Y . Liu, L. Li, S. Ren, R. Gao, S. Li, S. Chen, X. Sun, and L. Hou, “FETV: A benchmark for fine-grained evaluation of open-domain text-to-video generation,” in Proc. NeurIPS, vol. 36, 2023, pp. 62 352–62 387

    2023

  52. [60]

    Toward verifiable and repro- ducible human evaluation for text-to-image generation,

    M. Otani, R. Togashi, Y . Sawai, R. Ishigami, Y . Nakashima, E. Rahtu, J. Heikkil¨a, and S. Satoh, “Toward verifiable and repro- ducible human evaluation for text-to-image generation,” in Proc. CVPR, June 2023, pp. 14 277–14 286

    2023

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.