PromptEVC: Controllable Emotional Voice Conversion with Natural Language Prompts

Cheng Lu; Hao Yang; Shiyan Wang; Tengfei Song; Tianhua Qi; Wenming Zheng; Zhanglin Wu

REVIEW 4 major objections 6 minor 37 references

PromptEVC: Controllable Emotional Voice Conversion with Natural Language Prompts

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read PromptEVC claims that free-text descriptions alone can control emotional voice conversion at fine granularity—emotion category, intensity, mixed emotions, and prosody—without labels, reference audio, or numeric values, and that it beats…

desk verdict Text prompts for emotional voice conversion is a genuinely new capability and the system looks plausible, but the paper claims a comparative win on controllability that its own tables don't actually test. read the letter →

arxiv 2505.20678 v1 pith:7CUXAZ43 submitted 2025-05-27 eess.AS cs.SDeess.SP

Tianhua Qi , Shiyan Wang , Cheng Lu , Tengfei Song , Hao Yang , Zhanglin Wu , Wenming Zheng This is my paper · ORCID

classification eess.AScs.SDeess.SP

keywords emotionalvoiceconversionnaturallanguagepromptsfine-grainedemotioncontroldiffusionpromptmapperprosodymodelingmixedsynthesisspeakeridentitypreservationtext-conditionedspeech

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

The paper sets out to show that a voice-conversion system can be controlled entirely by a free-text description of the desired emotion, replacing fixed labels, reference recordings, and hand-set numeric values. It proposes a text-to-emotion pathway in which a text encoder produces a coarse emotion embedding and a diffusion-based prompt mapper refines it against emotion embeddings extracted from real speech, so the final embedding tracks words like 'very', 'slightly', and 'with a hint of surprise'. A prosody pipeline then adjusts rhythm using the linguistic content plus emotional cues, and a speaker encoder with a fundamental-frequency constraint preserves identity. On the TextrolSpeech corpus the paper reports better emotion conversion, intensity control, mixed-emotion synthesis, and prosody manipulation than label-, reference-, and value-based EVC baselines, with ablations attributing a large share of the gain to the prompt mapper. If the claim holds, users could simply type how they want an utterance to sound and the system would deliver it.

What carries the argument

The load-bearing mechanism is the two-stage text-to-emotion embedding pathway: a coarse text embedding produced by a pretrained language model, refined by a diffusion model that is jointly trained to reproduce reference emotion embeddings from speech. The diffusion prompt mapper is what makes open-vocabulary wording usable—without it, directly predicting emotion embeddings from text raises pitch error and lowers emotion-classification accuracy and naturalness. Supporting mechanisms are the prosody pipeline (deduplicated speech units, a duration regulator, and a prosody predictor) and an F0-constrained speaker encoder, which together keep content intelligible and identity stable while rhythm and pitch are edited.

What would settle it

Synthesize one neutral utterance under prompts that differ only in the stated intensity ('slightly happy', 'happy', 'very happy') and only in prosody words ('faster' vs 'slower'); if a pretrained emotion model's intensity scores and listener ratings do not move monotonically in the stated direction, the claimed fine-grained control does not hold.

Watch

Extended reading notes

Core claim

The authors' central claim is that natural-language prompts can serve as the sole control signal for fine-grained emotional voice conversion, provided the gap between text and speech is bridged in two stages. The emotion descriptor turns a prompt into a coarse embedding $e_{txt}$; the prompt mapper, a diffusion model trained jointly with reference embeddings $e_{ref}$ from a self-supervised speech-emotion model, refines this into $e_{pm}$, and only this text-conditioned embedding is used at inference. Prosody is handled separately: quantized self-supervised speech units provide linguistic tokens, a duration regulator adjusts rhythm from content and emotion, and a prosody predictor synthesizes natural pitch and energy. A speaker encoder with an F0 constraint keeps identity fixed while intonation changes. The paper reports that this design beats label-, reference-, and value-based controllable EVC baselines on emotion conversion, intensity control, mixed-emotion synthesis, and prosody manipulation, and its ablations show that removing the prompt mapper is the most damaging single change.

Load-bearing premise

The load-bearing premise is that the large-language-model rewrites of the training prompts preserve the original style-factor tags and the emotional content of the paired audio, because the emotion descriptor and prompt mapper are trained entirely on that alignment and no human validation of the rewrites is reported.

Editorial extensions

If this is right

Users can specify the target emotion in free text, such as 'very happy with a hint of surprise,' and get converted speech without selecting a reference clip or typing numeric values.
Because the prompt mapper is trained against reference speech embeddings, the same text interface can express emotional intensity, mixed emotions, pitch, speed, and volume within a single prompt.
The prosody pipeline keeps content intelligible during emotional edits; the paper reports lower character error rate than the label- and reference-based baselines.
The F0-constrained speaker encoder means emotional expression can be altered without the speaker identity drifting, which is needed for dubbing and assistive applications.

Reading between the lines

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

One consequence the paper leaves implicit is that the reference encoder may be needed only for training: since inference uses only the text-conditioned embedding, the same design could run without any reference speech at deployment, which suits streaming or on-device use.
The controllability claim is bounded by the five style factors of the training corpus; prompts describing states outside that coverage, such as 'bored but urgent' or non-English descriptions, are a natural stress test that the paper does not report.
Because the training prompts were rewritten by a large language model with no reported human validation, the intensity-control numbers should be read as conditional on those rewrites preserving the original style-factor tags.
The same text-conditioned diffusion-mapper design could plausibly extend to other suprasegmental speech attributes—politeness, emphasis, confidence—whenever paired text descriptions and speech are available.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The genuinely new thing here is bringing natural language prompts to emotional voice conversion. PromptVC and PromptTTS2 do this for style and TTS, but not for emotion specifically, and the combination of a RoBERTa descriptor, a diffusion-based prompt mapper that aligns text to emotion2vec reference embeddings, and a prosody control pipeline is a sensible way to get fine-grained control without labels, reference clips, or sliders. Credit where it's due: the architecture is coherent, the ablation study shows each removed component hurts in the direction you'd expect, and Table 1 consistently favors PromptEVC on MCD, CER, RMSE_F0, ACC_cls, and MOS. That is a real empirical result for a first system.

The soft spots are mostly about the gap between what the abstract claims and what the data actually show. The abstract says PromptEVC outperforms state-of-the-art methods in intensity control, mixed emotion synthesis, and prosody manipulation. But Tables 2 and 3 only report PromptEVC's own attribute classification accuracy; no baseline (Emovox, Mixed-EVC, ZEST, Textless-EVC) is run on those protocols. So the data support "PromptEVC produces recognizable attributes," not "PromptEVC beats the baselines on those attributes." That's a real overreach, and the stress-test note is right to flag it. The fix is either add baselines to the controllability evaluation or soften the language.

The next soft spot is the ChatGPT-4 prompt rewriting. The emotion descriptor and prompt mapper are trained on the rewritten text, and there's no human validation that the rewrites preserved intensity, speed, volume, or mixed-emotion annotations. That's a load-bearing assumption, and it's untreated. Also, the objective results in Table 1 are reported without variance or significance tests, and ACC_cls is attributed to FunASR, which is an ASR toolkit, not a speech emotion recognizer—likely a mis-citation. One corpus and no code or data artifacts limit reproducibility, though the demo page is a plus.

Underneath that, the central idea is credible and the system is not internally contradictory. The citation pattern is fine; self-cites to the authors' earlier EVC work are relevant and not hiding anything. The paper deserves a serious referee because it opens a useful direction and the core mapping is plausible, but it needs a revision that either delivers the missing baseline comparison for controllability or restates the claim, plus validation of the prompt rewriting.

Recommendation: send to peer review. It's not ready as is, but it's a solid workshop-to-conference submission with a novel capability and a fixable evaluation gap.

Referee Report

4 major / 6 minor

Summary. The paper proposes PromptEVC, an emotional voice conversion system that uses natural language prompts to control emotional expression. The system comprises a RoBERTa-based emotion descriptor, a diffusion-based prompt mapper that aligns text embeddings with emotion2vec reference embeddings, a prosody modeling and control module with a duration regulator and prosody predictor, and a speaker encoder with an F0 constraint. Experiments on the TextrolSpeech dataset compare PromptEVC against four baselines on global objective and subjective metrics (MCD, CER, RMSE_F0, ACC_cls, MOS naturalness, similarity) and include ablations. Controllability is assessed via classifier-based and listener-based accuracy on emotional intensity, mixed emotion, pitch, speaking speed, and volume.

Significance. If the claims hold, PromptEVC would be a valuable step toward flexible, text-driven control of emotional expression in voice conversion, removing the need for reference audio or numeric values at inference. The architecture is plausible and internally consistent: the components (VITS, HuBERT units, emotion2vec, diffusion conditioning) are integrated in a sensible way, and the ablations show that each proposed module contributes to the reported metrics. The main contribution is a new combination of existing techniques for the EVC setting, with a prompt-mapping mechanism that appears trainable with available paired data. However, the current experimental evidence does not support the comparative controllability claims, and the evaluation has statistical gaps that prevent the reported superiority from being established.

major comments (4)

[Section 3.4, Tables 2 and 3] The abstract and conclusion claim that PromptEVC outperforms state-of-the-art controllable EVC methods in intensity control, mixed emotion synthesis, and prosody manipulation, but Tables 2 and 3 report attribute classification accuracies for PromptEVC only. No baseline (Textless-EVC, ZEST, Emovox, Mixed-EVC) is evaluated with the same pre-trained classifier or the same five-participant listening protocol on E_in, E_mx, P, S, or V. The superiority for these three capabilities is therefore inferred from the global quality metrics in Table 1 rather than measured on the controllability tasks themselves. Please run the same controllability protocols on at least the most relevant baselines (Emovox for intensity, Mixed-EVC for mixed emotion) or revise the claim to state that PromptEVC enables control of these attributes rather than outperforming existing methods on them.
[Section 3.1, Data preparation] The ChatGPT-4 rewritten prompts are the sole training signal for the emotion descriptor and the prompt mapper, yet no validation is reported that the rewrites preserve the original five style factors (E_in, P, S, V, E_cg) or the emotional content of the paired audio. The rewrites also deliberately remove gender information, which is reasonable for the task, but the extent to which other factors are distorted is unknown. If the rewrites systematically alter intensity, speed, volume, or mixed-emotion annotations, the text-conditioned emotion embeddings will be trained on incorrect targets, and the controllability results in Tables 2 and 3 may not transfer to real user prompts. Please provide a human or automatic validation of the rewritten prompts (e.g., ratings against the original factor values, or a classifier-based consistency check) and report the agreement.
[Table 1 and Section 3.2] The objective metrics MCD, CER, RMSE_F0, and ACC_cls are reported as point estimates with no variance, no test set size, and no significance tests, and only MOS has confidence intervals. The comparison is also performed on a single corpus (TextrolSpeech). Under these conditions, the claimed superiority of PromptEVC over Emovox and Mixed-EVC on these metrics is not statistically established. Please report the number of test utterances, per-utterance or per-condition standard deviations, and perform significance testing (e.g., paired tests with bootstrap or Wilcoxon) for at least the primary metrics. Also specify how many speakers and which emotion categories are included in the evaluation set.
[Section 3.2, Table 1; Section 3.4, Table 2] ACC_cls is attributed to a 'pre-trained speech emotion recognition (SER) model [31]', but reference [31] is the FunASR speech recognition toolkit, not a SER model. This is either a mis-citation or a serious mismatch between the stated metric and the actual implementation. Please specify the exact emotion classification model used, its training data, and whether it was independently trained on the same TextrolSpeech distribution. Likewise, the pre-trained classifier used for Table 2 is not described in terms of architecture, training data, or whether it was trained on TextrolSpeech; without this information, the absolute accuracy values (77.58%, 61.25%, etc.) are difficult to interpret, and the reader cannot judge whether the classifier is sensitive enough to support the controllability claims.

minor comments (6)

[Section 2.1, Eq. (3)] The diffusion loss is written with a double vertical bar but no exponent; it should be the squared L2 norm. Please also define the dimensions of x_t and e_txt explicitly.
[Section 2.2, Eq. (4)] The duration loss expression `log cosh(\hat{y}_i - log(y_i+1))` is unconventional; the log-cosh loss is usually applied to the residual y - \hat{y}, not to a residual after logarithmic scaling. Please clarify the exact functional form and why this particular transformation is used.
[Section 3.1] The intensity cutoffs of 30% / 40% / 30% for high/medium/low are presented without justification. Please cite a source or provide a rationale (e.g., distributional analysis) for these thresholds.
[Table 3] The Ground Truth row has no entry for E_mx. Please clarify whether mixed-emotion ground truth is unavailable by design, and how the 60.04% accuracy for E_mx should be interpreted in that case.
[Figure 1] Figure 1 is quite dense and hard to read in the preprint. Please enlarge the figure and provide a more detailed caption that explains the data flow, especially for the prosody modeling and control module shown in Figure 1(b).
[Abstract and Conclusion] The phrase 'outperforms state-of-the-art controllable EVC methods' and 'significantly improves' are stronger than what the current experimental design supports, given the missing baseline comparisons and statistical tests noted above. Please soften these statements until the evidence is added.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the text-prompt mapping is a supervised cross-modal alignment task, and the central claims do not reduce to self-citations or fitted inputs.

full rationale

PromptEVC's derivation chain is self-contained. The emotion descriptor (RoBERTa plus linear projection) and prompt mapper (diffusion denoiser) are trained with the objective L_pm = E[||epsilon - epsilon_theta(x_t, e_txt, t)||^2] to reconstruct the emotion2vec reference embedding x0 from the text embedding e_txt; this is a standard supervised alignment between text and pre-trained speech-emotion representations, not an identity or a fit whose output is its own input. The prosody and speaker losses (L_rhy and L_spk) regress toward ground-truth durations and log-F0, and the ablation studies empirically measure degradation, none of which presupposes the target claim. The authors' prior works [6] and [7] appear only in the introduction as background examples of autoencoder-based and intensity-based EVC; they are not load-bearing for the natural-language-prompt contribution, so they do not constitute circularity. The skeptical concern that Tables 2 and 3 report controllability accuracy only for PromptEVC without baseline comparisons is a valid evidentiary limitation about the strength of the comparative claim, but it is not a circularity in the derivation. No equation in the paper equates the output to the input by construction, and no fitted parameter is renamed as a prediction.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

Ledger summary: the system rests on several domain assumptions about representation models and data alignment, plus a small number of hand-chosen hyperparameters. No new physical or conceptual entities are postulated beyond the learned modules, so invented_entities is empty.

free parameters (4)

K-means dictionary size = 100
Chosen by hand for HuBERT unit quantization; controls granularity of linguistic tokens and prosody modeling.
Intensity level thresholds = top 30%, middle 40%, bottom 30%
Partition of RAR scores into high/medium/low; follows [13] but is an arbitrary binning that defines the intensity control targets.
Pretraining and fine-tuning epochs = 500 + 200
Manual training schedule; final convergence and loss balance depend on it.
Diffusion noise schedule beta = not specified
Needed for Eqs. (1)-(3) but the numeric schedule is not reported, making reproduction harder.

assumptions (5)

standard math Eqs. (1)-(3) correctly describe score-based diffusion denoising and the training objective is standard.
The SDE/ODE pair and loss follow from score-based generative modeling; the paper does not derive them but relies on known results.
domain assumption emotion2vec reference embeddings are a valid and sufficient target representation of emotional speech for text-to-emotion mapping.
The prompt mapper is trained to reconstruct emotion2vec embeddings; if these embeddings miss prosodic or intensity dimensions, the text control is limited.
domain assumption HuBERT K-means tokens, after deduplication, encode linguistic content without entangling emotion or speaker information.
Duration regulator and prosody predictor use these tokens as content representation; emotion-specific variation is assumed to live in the emotion embedding and prosody predictor.
ad hoc to paper ChatGPT-4 rewritten prompts remain faithfully aligned with the original TextrolSpeech style factors and the audio content.
Section 3.1: prompts are rewritten to remove gender and add complexity, but no human validation is reported; the whole text-emotion mapping depends on this alignment.
domain assumption Objective and subjective metrics used (MCD, CER, RMSEF0, ACCcls, MOS) reflect emotion conversion quality and controllability.
The evaluation section interprets these metrics as evidence for the central claims; the ACCcls metric is described via a citation mismatch ([31] is FunASR, an ASR toolkit).

how reviews work

0 comments

Cite this review

Pith. "Pith review of PromptEVC: Controllable Emotional Voice Conversion with Natural Language Prompts." pith.science (2026). https://pith.science/paper/7CUXAZ43

@misc{pith2026250520678,
  author       = {Pith},
  title        = {Pith review of: PromptEVC: Controllable Emotional Voice Conversion with Natural Language Prompts},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7CUXAZ43}},
  note         = {Machine review of arXiv:2505.20678}
}

read the original abstract

Controllable emotional voice conversion (EVC) aims to manipulate emotional expressions to increase the diversity of synthesized speech. Existing methods typically rely on predefined labels, reference audios, or prespecified factor values, often overlooking individual differences in emotion perception and expression. In this paper, we introduce PromptEVC that utilizes natural language prompts for precise and flexible emotion control. To bridge text descriptions with emotional speech, we propose emotion descriptor and prompt mapper to generate fine-grained emotion embeddings, trained jointly with reference embeddings. To enhance naturalness, we present a prosody modeling and control pipeline that adjusts the rhythm based on linguistic content and emotional cues. Additionally, a speaker encoder is incorporated to preserve identity. Experimental results demonstrate that PromptEVC outperforms state-of-the-art controllable EVC methods in emotion conversion, intensity control, mixed emotion synthesis, and prosody manipulation. Speech samples are available at https://jeremychee4.github.io/PromptEVC/.

Figures

Figures reproduced from arXiv: 2505.20678 by the authors.

**Figure 1.** Diagram of proposed PromptEVC, depicting the overall architecture (a) and the prosody modeling and control module (b). decoder for waveform reconstruction. 2.1. Text-emotion mapping Although natural language prompts offer considerable variability over previous approaches, there remains an information mismatch compared to original speech signals, which convey emotions through suprasegmental features such as intona… view at source ↗

**Figure 2.** Mel-spectrograms and pitch contours of the converted audios for different natural language prompts: “Convert this neutral utterance into a happy/surprise tone” and “Inject some happiness into this sentence with a hint of surprise.” 3.4. Controllability of natural language prompts To demonstrate the controllability of natural language prompts in emotion conversion, we conduct both objective and subjective evaluation… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

37 extracted references · 36 canonical work pages

[31]

Hubert: Self-supervised speech repre sen- tation learning by masked prediction of hidden units,

W.-N. Hsu, B. Bolte, Y .-H. H. Tsai, K. Lakhotia, R. Salak hutdi- nov, and A. Mohamed, “Hubert: Self-supervised speech repre sen- tation learning by masked prediction of hidden units,” IEEE/ACM transactions on audio, speech, and language processing , vol. 29, pp. 3451–3460, 2021

work page 2021
[1]

happy with surprise

Introduction Emotional voice conversion (EVC) aims to modify the emo- tional tone of spoken utterances while preserving their linguistic content and speaker identity [1]. This technique facilitat es emo- tional communication, enhances user experiences in human- machine interactions, and contributes to more immersive vi r- tual environments [2]. Convention...

work page
[2]

PromptEVC: Controllable Emotional Voice Conversion with Natural Language Prompts

Proposed Method As illustrated in Figure 1(a), PromptEVC is built upon condi - tional variational autoencoder (CV AE), consisting of: 1) a text- emotion mapping module, 2) a prosody modeling and control module, 3) a speaker encoder for identity preservation, and 4) a arXiv:2505.20678v1 [eess.AS] 27 May 2025 Figure 1: Diagram of proposed PromptEVC, depicti...

work page Pith review arXiv 2025
[3]

Convert th is neutral utterance into a happy/surprise tone

Experiments 3.1. Experimental setup Datasets. We conduct emotion conversion using the Textrol- Speech dataset [29], which consists of 330 hours of audio, 236,203 pairs of natural text descriptions with correspond ing speech samples. The dataset includes 42,909 emotional spee ch samples, categorized as follows: angry (18.75%), contempt (6.25%), disgusted (...

work page
[4]

The results indi - cate that the emotional speech generated using natural lang uage prompts closely aligns with human expectations

For subjective evaluation, ﬁve participants annotate th e test audios alongside the ground truth, and the average classiﬁcation accuracy is calculated, as shown in Table 3. The results indi - cate that the emotional speech generated using natural lang uage prompts closely aligns with human expectations. Furthermo re, Figure 2 presents synthesized Mel spec...

work page
[5]

Conclusion In this paper, we propose PromptEVC to control subtle emotional expressions in emotional voice conversion (EVC) through natural language prompts. By incorporating emotio n descriptor, prompt mapper with prosody modeling and contro l module, PromptEVC effectively learns ﬁne-grained emotion al representations across multiple attributes. Experimen...

work page
[6]

Acknowledgment This work was supported in part by the National Key R&D Project under the Grant 2022YFC2405600, in part by the NSFC under the Grant U2003207, in part by the YESS Pro- gram by JSAST under the Grant JSTJ-2023-XH033, in part by the China Postdoctoral Science Foundation under the Gran t 2023M740600, in part by the Jiangsu Province Excellent Pos...

work page 2023
[7]

Emotional voice con - version: Theory, databases and esd,

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

work page 2022

Show all 37 references

[8]

An overview of affective speech synthesis and conversion i n the deep learning era,

A. Triantafyllopoulos, B. W. Schuller, G. ˙ Iymen, M. Sezgin, X. He, Z. Y ang, P . Tzirakis, S. Liu, S. Mertes, E. Andr´ e et al. , “An overview of affective speech synthesis and conversion i n the deep learning era,” Proceedings of the IEEE , vol. 111, no. 10, pp. 1355–1381, 2023

2023
[9]

Speaker-independent emotional voice conversio n via disentangled representations,

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

2022
[10]

Emotional voice con- version with semi-supervised generative modeling,

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

2023
[11]

Textless speech emotion conversion using discrete & decom - posed representations,

F. Kreuk, A. Polyak, J. Copet, E. Kharitonov, T.-A. Nguye n, M. Rivi` ere, W.-N. Hsu, A. Mohamed, E. Dupoux, and Y . Adi, “Textless speech emotion conversion using discrete & decom - posed representations,” in EMNLP 2022, 2022

2022
[12]

Pavits: Explo ring prosody-aware vits for end-to-end emotional voice convers ion,

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

2024
[13]

Towards realistic emotional voice conversion using controllable e motional intensity,

T. Qi, S. Wang, C. Lu, Y . Zhao, Y . Zong, and W. Zheng, “Towards realistic emotional voice conversion using controllable e motional intensity,” in Proc. Interspeech 2024, 2024, pp. 202–206

2024
[14]

Emotio nal voice conversion using multitask learning with text-to-sp eech,

T.-H. Kim, S. Cho, S. Choi, S. Park, and S.-Y . Lee, “Emotio nal voice conversion using multitask learning with text-to-sp eech,” in ICASSP 2020-2020 IEEE International Conference on Acousti cs, Speech and Signal Processing (ICASSP). IEEE, 2020, pp. 7774– 7778

2020
[15]

Seen and unseen emo- tional style transfer for voice conversion with a new emotio nal speech dataset,

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

2021
[16]

Msemotts: Multi-sc ale emotion transfer, prediction, and control for emotional sp eech synthesis,

Y . Lei, S. Y ang, X. Wang, and L. Xie, “Msemotts: Multi-sc ale emotion transfer, prediction, and control for emotional sp eech synthesis,” IEEE/ACM Transactions on Audio, Speech, and Lan- guage Processing, vol. 30, pp. 853–864, 2022

2022
[17]

Zero shot audio to audio emot ion transfer with speaker disentanglement,

S. Dutta and S. Ganapathy, “Zero shot audio to audio emot ion transfer with speaker disentanglement,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Sig nal Processing (ICASSP). IEEE, 2024, pp. 10 371–10 375

2024
[18]

V oice conversion for em o- tional speech: Rule-based synthesis with degree of emotion con- trollable in dimensional space,

Y . Xue, Y . Hamada, and M. Akagi, “V oice conversion for em o- tional speech: Rule-based synthesis with degree of emotion con- trollable in dimensional space,” Speech Communication, vol. 102, pp. 54–67, 2018

2018
[19]

Em otion intensity and its control for emotional voice conversion,

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

2022
[20]

Relative attributes,

D. Parikh and K. Grauman, “Relative attributes,” in 2011 Interna- tional conference on computer vision. IEEE, 2011, pp. 503–510

2011
[21]

Mixed-evc : Mixed emotion synthesis and control in voice conversion,

K. Zhou, B. Sisman, C. Busso, B. Ma, and H. Li, “Mixed-evc : Mixed emotion synthesis and control in voice conversion,” i n The Speaker and Language Recognition W orkshop (Odyssey 2024) , 2024, pp. 180–186

2024
[22]

Interpreting ambiguous emotiona l expressions,

E. Mower, A. Metallinou, C.-C. Lee, A. Kazemzadeh, C. Bu sso, S. Lee, and S. Narayanan, “Interpreting ambiguous emotiona l expressions,” in 2009 3rd International Conference on Affective Computing and Intelligent Interaction and W orkshops . IEEE, 2009, pp. 1–8

2009
[23]

De Beaugrande and W

R.-A. De Beaugrande and W. U. Dressler, Introduction to text lin- guistics. longman London, 1981, vol. 1

1981
[24]

V ocal expression of affect,

P . N. Juslin, K. R. Scherer, J. Harrigan, and R. Rosentha l, “V ocal expression of affect,” The new handbook of methods in nonverbal behavior research, pp. 65–135, 2005

2005
[25]

Prompttts: Control- lable text-to-speech with text descriptions,

Z. Guo, Y . Leng, Y . Wu, S. Zhao, and X. Tan, “Prompttts: Control- lable text-to-speech with text descriptions,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Sig nal Processing (ICASSP). IEEE, 2023, pp. 1–5

2023
[26]

Prompttts 2: Describing and generating voices with text prompt,

Y . Leng, Z. Guo, K. Shen, Z. Ju, X. Tan, E. Liu, Y . Liu, D. Y a ng, K. Song, L. He et al. , “Prompttts 2: Describing and generating voices with text prompt,” in The Twelfth International Conference on Learning Representations
[27]

Promptvc: Flexible stylistic voice conve rsion in latent space driven by natural language prompts,

J. Y ao, Y . Y ang, Y . Lei, Z. Ning, Y . Hu, Y . Pan, J. Yin, H. Zhou, H. Lu, and L. Xie, “Promptvc: Flexible stylistic voice conve rsion in latent space driven by natural language prompts,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Spe ech and Signal Pr...

2024
[28]

Neutr al to target emotion conversion using source and suprasegmental infor- mation

D. Govind, S. M. Prasanna, and B. Y egnanarayana, “Neutr al to target emotion conversion using source and suprasegmental infor- mation.” in Interspeech, 2011, pp. 2969–2972

2011
[29]

emotion2vec: Self-supervised pre-training for speech em otion representation,

Z. Ma, Z. Zheng, J. Y e, J. Li, Z. Gao, S. Zhang, and X. Chen, “emotion2vec: Self-supervised pre-training for speech em otion representation,” in Findings of the Association for Computational Linguistics: ACL 2024 . Association for Computational Linguis- tics, 2024, pp. 15 747–15 760

2024
[30]

Roberta: A robustly optimized bert pretraining approach (2019),

L. Yinhan, O. Myle, G. Naman, D. Jingfei, J. Mandar, C. Da nqi, L. Omer, and L. Mike, “Roberta: A robustly optimized bert pretraining approach (2019),” arXiv preprint arXiv:1907.11692 , 2019

2019 arXiv
[32]

On generative spoken language modeling from raw au- dio,

K. Lakhotia, E. Kharitonov, W.-N. Hsu, Y . Adi, A. Polyak , B. Bolte, T.-A. Nguyen, J. Copet, A. Baevski, A. Mohamed et al. , “On generative spoken language modeling from raw au- dio,” Transactions of the Association for Computational Linguis - tics, vol. 9, pp. 1336–1354, 2021

2021
[33]

Prosody and emotions,

S. Mozziconacci, “Prosody and emotions,” in Speech Prosody 2002, International Conference, 2002

2002
[34]

Efﬁcient acoustic parameters for speaker r ecognition,

J. J. Wolf, “Efﬁcient acoustic parameters for speaker r ecognition,” The Journal of the Acoustical Society of America , vol. 51, no. 6B, pp. 2044–2056, 1972

1972
[35]

Textrolspeech: A text style control speech corpus with codec language text-to-speech models,

S. Ji, J. Zuo, M. Fang, Z. Jiang, F. Chen, X. Duan, B. Huai, and Z. Zhao, “Textrolspeech: A text style control speech corpus with codec language text-to-speech models,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Sig nal Processing (ICASSP). IEEE...

2024
[36]

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

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

2021
[37]

Funasr: A fundamental end-to-end speec h recognition toolkit,

Z. Gao, Z. Li, J. Wang, H. Luo, X. Shi, M. Chen, Y . Li, L. Zuo , Z. Du, and S. Zhang, “Funasr: A fundamental end-to-end speec h recognition toolkit,” in Interspeech 2023, 2023, pp. 1593–1597

2023

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [31]

Hubert: Self-supervised speech repre sen- tation learning by masked prediction of hidden units,

W.-N. Hsu, B. Bolte, Y .-H. H. Tsai, K. Lakhotia, R. Salak hutdi- nov, and A. Mohamed, “Hubert: Self-supervised speech repre sen- tation learning by masked prediction of hidden units,” IEEE/ACM transactions on audio, speech, and language processing , vol. 29, pp. 3451–3460, 2021

work page 2021

[2] [1]

happy with surprise

Introduction Emotional voice conversion (EVC) aims to modify the emo- tional tone of spoken utterances while preserving their linguistic content and speaker identity [1]. This technique facilitat es emo- tional communication, enhances user experiences in human- machine interactions, and contributes to more immersive vi r- tual environments [2]. Convention...

work page

[3] [2]

PromptEVC: Controllable Emotional Voice Conversion with Natural Language Prompts

Proposed Method As illustrated in Figure 1(a), PromptEVC is built upon condi - tional variational autoencoder (CV AE), consisting of: 1) a text- emotion mapping module, 2) a prosody modeling and control module, 3) a speaker encoder for identity preservation, and 4) a arXiv:2505.20678v1 [eess.AS] 27 May 2025 Figure 1: Diagram of proposed PromptEVC, depicti...

work page Pith review arXiv 2025

[4] [3]

Convert th is neutral utterance into a happy/surprise tone

Experiments 3.1. Experimental setup Datasets. We conduct emotion conversion using the Textrol- Speech dataset [29], which consists of 330 hours of audio, 236,203 pairs of natural text descriptions with correspond ing speech samples. The dataset includes 42,909 emotional spee ch samples, categorized as follows: angry (18.75%), contempt (6.25%), disgusted (...

work page

[5] [4]

The results indi - cate that the emotional speech generated using natural lang uage prompts closely aligns with human expectations

For subjective evaluation, ﬁve participants annotate th e test audios alongside the ground truth, and the average classiﬁcation accuracy is calculated, as shown in Table 3. The results indi - cate that the emotional speech generated using natural lang uage prompts closely aligns with human expectations. Furthermo re, Figure 2 presents synthesized Mel spec...

work page

[6] [5]

Conclusion In this paper, we propose PromptEVC to control subtle emotional expressions in emotional voice conversion (EVC) through natural language prompts. By incorporating emotio n descriptor, prompt mapper with prosody modeling and contro l module, PromptEVC effectively learns ﬁne-grained emotion al representations across multiple attributes. Experimen...

work page

[7] [6]

Acknowledgment This work was supported in part by the National Key R&D Project under the Grant 2022YFC2405600, in part by the NSFC under the Grant U2003207, in part by the YESS Pro- gram by JSAST under the Grant JSTJ-2023-XH033, in part by the China Postdoctoral Science Foundation under the Gran t 2023M740600, in part by the Jiangsu Province Excellent Pos...

work page 2023

[8] [7]

Emotional voice con - version: Theory, databases and esd,

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

work page 2022