SEmoEdit: Probing and Harnessing the Editability of Pre-trained Speech Flows

Tianxin Xie; Pengfei Zhang; Kai Jiang; Zelin Zhao; Li Liu

cs.SDSep 28, 2026

SEmoEdit: Probing and Harnessing the Editability of Pre-trained Speech Flows

Authors: Tianxin Xie, Pengfei Zhang, Kai Jiang, Zelin Zhao, Li Liu

Organizations: The Hong Kong University of Science and Technology (Guangzhou)

Abstract

Existing training-based speech emotion editing methods often require substantial task-specific training and can be unstable. This motivates us to investigate whether the pretrained generative dynamics of large-scale text-to-speech (TTS) models can be directly manipulated for training-free emotion editing. To answer this question, we probe the editability of pretrained flow-matching and hybrid TTS models by constructing a controlled test set and systematically diagnosing editing effects along the generative trajectory. Our analysis reveals that pretrained TTS models are substantially editable in emotion, but such editability is architecture- and trajectory-dependent and can be disrupted by early flow-matching steps, while cross-speaker emotion transport carries additional acoustic attributes beyond emotion. To address these limitations, we propose SEmoEdit, the first training-free framework that formulates emotion editing as dynamic velocity transport between source and target emotions, enabling robust, flow-based speech emotion editing directly within pretrained TTS models. SEmoEdit unifies three core operations: emotion replacement, emotion erasure, and continuous emotion interpolation, requiring neither parameter updates nor task-specific optimization. To systematically evaluate these capabilities, we introduce SEmoEditBench, a dataset comprising 600 editing cases, and conduct extensive experiments across state-of-the-art (SOTA) models and backbones. Our results show that SEmoEdit is highly effective and broadly applicable, outperforming existing training-based and activation-steering methods. Ultimately, this work reveals that pretrained speech flows possess rich, latent emotion-editing capabilities, providing useful guidance for real applications. Code, benchmark, and Audio samples are available at https://github.com/imxtx/SEmoEdit.

Figures & tables

Figure 1: Target emotion2vec ( Ma et al., 2024 ) probability, change in transcription error (

\Delta

WER, in percentage points; pp), and change in speaker similarity (

\Delta

S-SIM) before and after editing.

Figure 2: The first two columns show onset drift (top) and WER change from the source in percentage points (bottom) versus retained emotion gain relative to full-step editing. The green region denotes $\geq$ 90% gain retention. The third column demonstrates the onset drift problem.

Measure	F5-TTS	CosyVoice 2
S-SIM source $\downarrow$	$-0.403$	$-0.378$
S-SIM target $\uparrow$	$0.331$	$0.342$
Emotion gain (pp) $\uparrow$	$45.14$	$52.11$
F0 median gain (semitones) $\uparrow$	$4.565$	$7.075$
F0 range gain (semitones) $\uparrow$	$1.433$	$2.769$
Joint (/80) *	70	73

Table 1: Cross-speaker emotion editing induces concurrent attribute changes.

Figure 3: Framework of SEmoEdit for dynamic speech emotion editing.

Category	Method	Backbone	Metrics
Emotion replacement			TEP $\uparrow$	SES $\uparrow$	E-SIM $\uparrow$	DES $\uparrow$	$\Delta$ WER $\downarrow$	S-SIM $\uparrow$	UTMOS $\uparrow$
Training- based	Step-Audio-EditX	–	0.222	0.445	0.521	0.550	-0.048	0.567	3.091
	dots.tts.edit	–	0.434	0.600	0.669	0.713	-0.035	0.272	2.381
	Auk	–	0.259	0.372	0.539	0.541	0.073	0.631	2.639
Activation Steering	CoCoEmo	CosyVoice 2	0.082	0.192	0.446	0.399	-0.015	0.715	3.061
	CoCoEmo	IndexTTS2	0.035	0.127	0.402	0.269	-0.024	0.776	2.659

Table 2: Main results on the same-dataset same-speaker setting of SEmoEditBench. The top three results for each metric are highlighted in bold with dark, medium, and light pink backgrounds representing the first , second , and third best performances, respectively.

			Emotion replacement			Emotion erasure			Intensity control
Category	Method	Backbone	SS-MOS $\uparrow$	ES-MOS $\uparrow$	N-MOS $\uparrow$	SS-MOS $\uparrow$	ES-MOS $\uparrow$	N-MOS $\uparrow$	EIC-MOS $\uparrow$
Training- based	Step-Audio-EditX	–	3.67	2.88	3.75	4.20	2.35	4.00	–
	Auk	–	3.38	2.80	3.52	4.00	2.95	4.05	–
	dots.tts.edit	–	3.52	3.27	3.77	3.55	3.15	3.65	–
Activation Steering	CoCoEmo	IndexTTS2	4.42	1.88	3.98	–	–	–	1.65
Activation Steering	EmoSteer-TTS	IndexTTS2	4.35	1.75	4.12	4.50	1.80	4.25	1.30

Table 3: Subjective evaluation results. The first , second , and third place are highlighted.

Figure 4: Left: SEmoEdit ID/OOD generalization across backbones. Right: Onset drift over 600 cases; lower mean and median absolute drift are better, while a higher fraction within 50 ms is better.

Figure 5: Sensitivity to the number of coupled noise samples per editing step.

Figure 6: Mean TEP trajectories before and after bridging.

Backbone	Variant	WER/CER $\downarrow$	S-SIM $\uparrow$	UTMOS $\uparrow$
F5-TTS	Raw	0.342	0.418	2.151
F5-TTS	Bridged	0.205	0.431	2.411
CosyVoice 2	Raw	0.360	0.349	2.134
CosyVoice 2	Bridged	0.201	0.457	2.780
IndexTTS2	Raw	0.823	0.379	1.918
IndexTTS2	Bridged	0.603	0.440	2.652

Table 4: Objective metrics before and after emotion bridging, averaged over 90 intensity editing cases.

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 7: F5-TTS conditioning. The source-generated mel segment is linearly interpolated to the length of the target-generated mel segment.

Figure 8: CosyVoice 2 conditioning. Each branch uses its own reference mel and speaker embedding, together with the generated mel segment under that reference. The source branch’s generated mel is linearly interpolated to the target length, while the target branch remains unchanged. The two branches share a Gaussian noise sequence of length $\max(P_{s},P_{t})$ , which is right-aligned to each reference prefix.

Model	Emotion	Before (%, $\uparrow$ )	After (%, $\uparrow$ )	$\Delta$ (pp, $\uparrow$ )
F5-TTS	Happy	5.0456	64.8676	+59.8219
	Angry	19.9294	77.8099	+57.8805
	Sad	19.8356	60.8144	+40.9788
	Surprise	0.2028	39.6302	+39.4274
	Mean (80 cases)	11.2534	60.7805	+49.5272
CosyVoice 2	Happy	10.0841	70.5735	+60.4894

Appendix

Table 5: Target-emotion probability before and after editing. Values are percentages and

\Delta

is in percentage points (pp). CosyVoice uses the aligned-source baseline.

Model	Emotion	Before (%, $\downarrow$ )	After (%, $\downarrow$ )	$\Delta$ (pp, $\downarrow$ )
F5-TTS	Happy	18.0595	15.3095	-2.7500
	Angry	20.3591	9.4702	-10.8889
	Sad	9.7123	8.9385	-0.7738
	Surprise	12.4702	16.5575	+4.0873
	Mean (80 cases)	15.1503	12.5689	-2.5813
CosyVoice 2	Happy	8.8095	13.8452	+5.0357

Appendix

Table 6: Per-case-averaged WER before and after editing. Positive changes indicate degradation. CosyVoice uses the aligned-source baseline; its whole-pipeline comparison is reported in the text.

Model	Emotion	Before $\uparrow$	After $\uparrow$	$\Delta$ $\uparrow$
F5-TTS	Happy	0.6522	0.5966	-0.0556
	Angry	0.6629	0.5695	-0.0934
	Sad	0.6591	0.6186	-0.0405
	Surprise	0.6878	0.5499	-0.1379
	Mean (80 cases)	0.6655	0.5836	-0.0819
CosyVoice 2	Happy	0.6454	0.5716	-0.0738

Appendix

Table 7: Emotion-matched S-SIM defined in Eq. 12 . Before uses the neutral reference; after uses the target-emotion reference. CosyVoice before is the unedited generated speech before length alignment, so changes include alignment.

\Delta

is an absolute cosine difference.

Model	Editing prefix	Emotion gain (pp)	Onset drift (ms)	$\Delta$ WER (pp)
F5-TTS	First 4 of 32 steps	0.015	0.38	+0.21
F5-TTS	All 32 steps	53.78	308.33	$-0.92$
CosyVoice 2	First 3 of 10 steps	3.43	22.21	+2.83
CosyVoice 2	First 6 of 10 steps	30.37	87.83	+88.88
CosyVoice 2	All 10 steps	56.09	245.42	+1.40

Appendix

Table 8: Early-stop editing results. Editing begins at the first editing step and terminates after the indicated prefix. Results are averaged over 80 cases and three editing-noise repeats.

Model	Editing start	Gain retained (%)	Onset drift (ms)	$\Delta$ WER (pp)
F5-TTS	Full-step	100.0	308.33	$-0.92$
	Skip 4 steps	99.5	97.33	$-4.95$
CosyVoice 2	Full-step	100.0	245.42	$+1.40$
	Skip 1 step	100.3	260.29	$+0.67$
	Skip 3 steps	51.0	44.25	$+55.75$

Appendix

Table 9: Selected delayed starts, averaged over cases and editing-noise repeats. Gain retention is relative to full-step editing; onset drift and

\Delta

WER are relative to the decoded, length-aligned source.

Figure 9: Complete delayed-start scans for F5-TTS and CosyVoice 2. Columns show emotion gain (pp), onset drift (ms), and

\Delta

WER (pp), measured relative to the decoded, length-aligned source. Bands indicate pointwise 95% speaker-bootstrap intervals. The horizontal axis is the ODE step.

Model	Prefix	$t$	Emotion gain (pp)	Onset drift (ms)	$\Delta$ WER (pp)
F5-TTS	First 4 / 32	0.019	0.015	0.38	+0.21
F5-TTS	First 14 / 32	0.227	6.96	38.54	+1.31
F5-TTS	First 28 / 32	0.805	50.15	275.04	+6.03
F5-TTS	All 32 / 32	1.000	53.78	308.33	$-0.92$
CosyVoice 2	First 3 / 10	0.109	3.43	22.21	+2.83
CosyVoice 2	First 6 / 10	0.412	30.37	87.83	+88.88

Appendix

Table 10: Early-stop analysis. Editing starts at the first step and stops after the indicated prefix; values are means over 80 cases and three editing-noise repeats.

Figure 10: Early-stop scans for F5-TTS and CosyVoice 2. Columns report emotion gain, onset drift, and

\Delta

WER. Bands show 95% speaker-bootstrap intervals; dashed lines indicate full-step means.

Model	Block	Only block			Skip block
		Gain	Drift	$\Delta$ WER	Gain	Drift	$\Delta$ WER
F5-TTS	1	6.96	38.54	1.31	4.66	53.71	$-0.97$
	2	1.17	14.17	0.60	36.00	205.38	13.88
	3	0.72	0.50	0.25	40.27	253.75	8.10
	4	0.23	2.62	$-0.25$	49.26	267.79	4.95
	5	0.36	7.38	$-0.24$	50.15	275.04	6.03

Appendix

Table 11: Block ablations. Values are emotion gain (pp), onset drift (ms), and

\Delta

WER (pp).

Figure 11: F5-TTS block and editing-strength ablations.

Figure 12: CosyVoice 2 block and editing-strength ablations.

Task	Setting	Corpus	Cases	Construction
Replacement	Same dataset, same speaker	ESD, IEMOCAP	120	80 ESD and 40 IEMOCAP cases; sources and targets balanced across Neutral, Happy, Sad, Angry, and Surprise; Chinese and English in ESD.
Replacement	Same dataset, cross speaker	ESD, IEMOCAP	120	Same source cases as the same-speaker split, but the target reference comes from a different speaker of the same corpus.
Replacement	Cross dataset, cross speaker	ESD, IEMOCAP	80	Sources from one corpus and target references from the other; all English.
Erasure	Same dataset, same speaker	ESD, IEMOCAP	56	32 ESD and 24 IEMOCAP cases; source emotions Happy, Sad, Angry, and Surprise, all mapped to Neutral.
Erasure	Same dataset, cross speaker	ESD, IEMOCAP	56	Same source cases, target reference from a different speaker of the same corpus.
Erasure	Cross dataset, cross speaker	ESD, IEMOCAP	40	Sources from one corpus and target references from the other; all English.

Appendix

Table 12: SEmoEditBench task composition. Each task is evaluated under same-dataset same-speaker, same-dataset cross-speaker, and cross-dataset cross-speaker settings.

Symbol	Meaning
$x_{s}$ , $x_{e}$ , $x_{t}$	Source, edited, and paired-target waveforms, respectively
$e_{\mathrm{src}}$ , $e_{\mathrm{tgt}}$	Source and target emotion labels
$y$	Manifest transcript (ground-truth text)
$P(e\mid x)$	emotion2vec+ Large posterior probability for emotion $e$ given waveform $x$
$f_{E}(x)$	emotion2vec+ Large embedding of waveform $x$
$A(x)$	ASR transcript of waveform $x$ produced by Whisper-large-v3

Appendix

Table 13: Notation used in the metric definitions.

Metric	Definition	Applicability
Editing success
Target emotion probability (TEP) $\uparrow$	$P(e_{\mathrm{tgt}}\mid x_{e})$	Replacement
Neutral probability (NP) $\uparrow$	$P(\mathrm{Neutral}\mid x_{e})$	Erasure
Source emotion suppression (SES) $\uparrow$	$P(e_{\mathrm{src}}\mid x_{s})-P(e_{\mathrm{src}}\mid x_{e})$	Replacement, erasure
Embedding-based effective intensity control (EIC-Emb) $\uparrow$	Mean over all pairs $i<j$ of $\operatorname{sgn}(a_{j}-a_{i})\cdot\min(1,\max(0,\|a_{j}-a_{i}\|/\tau))$ , $\tau=1$	Intensity control (paired target)
Emotion similarity (E-SIM) $\uparrow$	$\cos\!\left(f_{E}(x_{e}),f_{E}(x_{t})\right)$	Splits with paired targets

Appendix

Table 14: Metrics used in SEmoEditBench. Upward and downward arrows indicate whether higher or lower values are preferred.

Category	Method	Backbone	Setting	TEP $\uparrow$	SES $\uparrow$	E-SIM $\uparrow$	DES $\uparrow$	$\Delta$ WER $\downarrow$	S-SIM $\uparrow$	UTMOS $\uparrow$
Training- based	Step-Audio-EditX	–	ID-SS	0.222	0.445	0.521	0.550	-0.048	0.567	3.091
			ID-CS	0.196	0.420	0.519	0.543	-0.021	0.561	3.053
			OOD	0.211	0.452	0.526	0.575	-0.026	0.519	3.142
	Auk	–	ID-SS	0.259	0.372	0.539	0.541	0.073	0.631	2.639
			ID-CS	0.259	0.372	0.539	0.541	0.073	0.631	2.670
			OOD	0.312	0.353	0.586	0.580	0.103	0.580	2.753

Appendix

Table 15: Complete emotion-replacement results under both in-distribution settings and the out-of-distribution setting.

Category	Method	Backbone	Setting	NP $\uparrow$	SES $\uparrow$	E-SIM $\uparrow$	DES $\uparrow$	$\Delta$ WER $\downarrow$	S-SIM $\uparrow$	UTMOS $\uparrow$
Training- based	Step-Audio-EditX	–	ID-SS	0.235	0.388	0.566	0.655	-0.064	0.577	3.220
			ID-CS	0.347	0.488	0.606	0.688	-0.028	0.586	3.133
			OOD	0.269	0.465	0.746	0.818	-0.070	0.561	3.197
	Auk	–	ID-SS	0.229	0.311	0.528	0.582	0.014	0.676	2.859
			ID-CS	0.229	0.311	0.528	0.582	0.014	0.676	2.881
			OOD	0.272	0.371	0.591	0.585	0.033	0.651	2.959

Appendix

Table 16: Complete emotion-erasure results under both in-distribution settings and the out-of-distribution setting. CoCoEmo is omitted because it does not support emotion erasure.

Category	Method	Backbone	Setting	EIC-Emb $\uparrow$	$\Delta$ WER α=1 $\downarrow$	S-SIM α=1 $\uparrow$	UTMOS α=1 $\uparrow$
Activation- steering	CoCoEmo	CosyVoice 2	ID-SS	0.041	0.044	0.621	3.234
			ID-CS	0.023	0.006	0.648	3.206
			OOD	0.057	0.044	0.595	2.990
	CoCoEmo	IndexTTS2	ID-SS	0.017	0.333	0.703	2.294
			ID-CS	0.007	0.378	0.699	2.344
			OOD	0.026	0.356	0.688	2.314

Appendix

Table 17: Complete emotion-intensity-control results under both in-distribution settings and the out-of-distribution setting. Training-based baselines are omitted because they do not support continuous intensity control.

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Sep 24, 2026, cs.SDJ/K move · Enter open · S save

EditVoice: Variable-Length Non-Autoregressive Zero-Shot TTS and Speech Editing with Edit Flows

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Flow-Matching Text-To-Speech

Open paper AskPDF arXiv

Recent non-autoregressive (NAR) zero-shot text-to-speech (TTS) models generate in parallel but typically require the target sequence length to be specified before generation. We introduce EditVoice, to our knowledge the first variable-length NAR zero-shot TTS model, which uses Edit Flows to jointly update speech content and sequence length through insertions, deletions, and substitutions. EditVoice adopts speech-infilling training, which unifies zero-shot TTS and text-based speech editing and allows both prefix and suffix speech prompt placements at inference. We introduce Complementary Prompt Sampling (CPS) to leverage the complementary Edit Flow predictions induced by the two prompt placements. We further find that EditVoice can edit source and model-generated speech beyond its training sources. We use this generalization for end-to-end editing and training-free post-generation refinement. With the Edit Flow model trained on 10K h of GigaSpeech, EditVoice demonstrates competitive zero-shot TTS performance on Seed-TTS Eval EN and LibriSpeech-PC and speech editing performance on RealEdit.

Abstract

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