Edit Who Speaks, Control How They Speak: Global Timbre Editing and Local Instruction Control for TTS

Junchuan Zhao; Chenglin Xu; Wei Zeng; Haoyang Li; Yiwen Guo; Ye Wang

cs.SDOct 8, 2026

Edit Who Speaks, Control How They Speak: Global Timbre Editing and Local Instruction Control for TTS

Authors: Junchuan Zhao, Chenglin Xu, Wei Zeng, Haoyang Li, Yiwen Guo, Ye Wang

Organizations: National University of Singapore · LIGHTSPEED · Nanyang Technological University · Independent Researcher

Abstract

Instruction-based text-to-speech (TTS) offers control over voice characteristics and speech expression through interfaces including voice cloning and text-based voice design. Voice cloning reproduces a reference voice, whereas text-based voice design creates a voice from a natural-language description. However, neither interface directly enables users to modify the timbre of a given reference and synthesize speech with the modified voice. Meanwhile, utterance-level expressive instructions leave changes across individual text segments underspecified. We introduce \textbf{EDICT}, a framework that unifies global timbre editing and local expressive control by using an edited acoustic reference to anchor voice identity across segments. To enable synthesis with an instruction-edited voice, EDICT combines reference audio with structured timbre edits to generate an edited reference in codec-token space. This representation serves as a shared voice anchor for a frozen TTS backbone, allowing segment-specific natural-language instructions to guide expression. To accommodate instruction changes while supporting acoustic continuity, EDICT rebuilds the KV cache at each segment boundary, refreshing instruction conditioning while retaining bounded acoustic context from previously generated speech. Evaluations on our proposed TimbreEdit-Bench and IntraTTS-Bench demonstrate improved timbre editing and a favorable balance between local instruction adherence, speaker consistency, and transition quality. Audio demos are available.

Figures & tables

Figure 1: EDICT combines global voice editing with local delivery control. The trainable editor uses the source voice and global edit instruction to generate an edited codec reference. This reference conditions every segment of the frozen TTS backbone, whose KV cache is rebuilt at instruction boundaries using selected acoustic context. The segments form one continuous utterance.

Figure 2: Segment-boundary KV cache reconstruction. Each non-final <EOS> triggers a fresh prefill containing the next instruction and text, the shared edited reference, and acoustic inputs from the first $S$ and most recent $K$ generated positions. The upper-left panel shows replacement of the old cache; the upper-right panel shows causal attention after reconstruction. The lower timeline illustrates successive instruction switches within one codec stream.

Figure 3: Benchmark construction and evaluation. Left: TimbreEdit-Bench pairs source and target voices with editing instructions. Right: IntraTTS-Bench aligns delivery instructions with successive text segments.

System	Instruction	Content and quality			WavLM			ERes2Net
System	Instruction	WER/CER (%) $\downarrow$	UTMOS $\uparrow$	DNSMOS $\uparrow$	SIM-T $\uparrow$	SIM-R	$\Delta\uparrow$	SIM-T $\uparrow$	SIM-R	$\Delta\uparrow$
Instruction-conditioned systems
Qwen3-TTS-VD	Structured	4.5 / 0.3	3.861	3.946	0.701	0.871	$\underline{-0.170}$	0.450	0.745	$\underline{-0.295}$
Qwen3-TTS-VD	Descriptive	3.0 / 0.3	3.830	3.965	0.676	0.832	$\underline{-0.156}$	0.435	0.714	$\underline{-0.279}$
Qwen3-TTS-Base	Structured	5.1 / 1.9	3.742	3.805	0.632	0.950	$-0.318$	0.431	0.845	$-0.414$
Qwen3-TTS-Base	Descriptive	3.3 / 1.1	3.756	3.776	0.637	0.950	$-0.313$	0.434	0.849	$-0.415$

Table 1: Objective results on TimbreEdit-Bench. WER/CER reports English WER / Chinese CER in percent. SIM-T/R denotes target/source similarity;

\Delta=\mathrm{SIM\text{-}T}-\mathrm{SIM\text{-}R}

. Best and second-best scores within each instruction form are bold and underlined , with CER and WER ranked separately; SIM-R is unranked. Target-reference voice conversion receives target audio and is evaluated separately on a same-text subset, with its best similarity and

\Delta

scores in bold .

System	Instruction	Up Acc. $\uparrow$	Down Acc. $\uparrow$	Category Acc. $\uparrow$	Preservation $\uparrow$
Qwen3-TTS-VD	Structured	67.0	64.3	53.2	83.3
Qwen3-TTS-VD	Descriptive	60.0	63.0	49.8	85.0
Qwen3-TTS-Base	Structured	62.3	54.5	46.4	45.7
Qwen3-TTS-Base	Descriptive	66.0	54.5	50.0	45.6
CosyVoice 2	Structured	68.1	65.5	57.7	68.2
CosyVoice 2	Descriptive	72.2	73.3	69.0	66.2

Table 2: Attribute-operation accuracy (%) on 500 TimbreEdit-Bench requests. Gemini compares source and generated audio without the target recording. Up/Down assesses ordinal direction, Category assesses categorical targets, and Preservation assesses unchanged attributes. Best and second-best scores within each instruction form are bold and underlined .

System	Control (%)			Consistency	Content and quality
System	SegAcc $\uparrow$	EmoAcc $\uparrow$	Trans $\uparrow$	SIM-I $\uparrow$	WER/CER (%) $\downarrow$	UTMOS $\uparrow$	DNSMOS $\uparrow$
Qwen3-TTS-VD (Concat.)	92.44	92.66	60.68	0.658	4.5 / 0.9	3.266	3.939
Qwen3-TTS-VD (Joint)	66.80	66.37	70.66	0.922	3.8 / 1.3	3.794	3.952
Qwen3-TTS-VD (TED-TTS)	74.25	74.02	78.03	0.911	4.5 / 1.7	3.655	3.853
Qwen3-TTS-VD (EDICT-Stage II)	82.30	83.60	82.00	0.915	5.1 / 1.8	3.631	3.930
CosyVoice 2 (Concat.)	62.43	62.73	31.80	0.917	6.5 / 3.6	3.916	3.866

Table 3: Intra-utterance instruction control on IntraTTS-Bench. SegAcc, EmoAcc, and Trans measure instruction adherence, emotion realization, and transition quality in percent. SIM-I measures inter-segment speaker similarity; WER/CER reports English WER / Chinese CER in percent. Best and second-best scores within each backbone are bold and underlined , with CER and WER ranked separately.

Configuration	Control (%)		Content and quality			Consistency	SIM-T $\uparrow$
Configuration	SegAcc $\uparrow$	Trans $\uparrow$	WER/CER (%) $\downarrow$	UTMOS $\uparrow$	DNSMOS $\uparrow$	SIM-I $\uparrow$	WavLM	ERes2Net
Qwen3-TTS-VD (Joint)	60.19	70.07	3.6 / 1.2	3.339	3.869	0.918	0.683	0.395
Qwen3-TTS-VD (Concat.)	86.43	56.69	4.5 / 0.8	3.669	3.895	0.884	0.694	0.405
EDICT Stage I + Concat.	91.17	58.47	4.8 / 1.1	3.296	3.739	0.881	0.749	0.598
EDICT	81.62	83.45	5.1 / 1.9	3.795	3.921	0.917	0.788	0.609

Table 4: Joint timbre and delivery control with Qwen3-TTS-VD. Requests combine TimbreEdit-Bench voice edits with IntraTTS-Bench texts and delivery instructions. SIM-T compares the complete output with the target voice; SIM-I measures inter-segment speaker similarity. WER/CER reports English WER / Chinese CER in percent. Best and second-best scores are bold and underlined , with CER and WER ranked separately.

Figure 4: Subjective evaluation of timbre editing and local control. Bars show mean five-point ratings from 19 listeners; error bars are computed from 95% bootstrap confidence intervals over audio items. Each system has 10 items in (a) and 12 in (b). EDICT is highlighted in coral; all variants in (b) use Qwen3-TTS-VD.

\dagger

Seed-VC receives target audio and is not rated for instruction-based edit accuracy.

Configuration	Instruction	WER/CER (%) $\downarrow$	UTMOS $\uparrow$	DNSMOS $\uparrow$	SIM-T $\uparrow$
Configuration	Instruction	WER/CER (%) $\downarrow$	UTMOS $\uparrow$	DNSMOS $\uparrow$	WavLM	ERes2Net
SFT (0.5M)	Structured	3.9 / 1.5	3.689	3.895	0.736	0.498
SFT (0.5M)	Descriptive	4.2 / 1.5	3.708	3.885	0.698	0.481
SFT (1M)	Structured	4.1 / 0.9	3.936	3.896	0.757	0.561
SFT (1M)	Descriptive	3.9 / 0.9	3.666	3.913	0.693	0.556
DPO ( $M=4$ )	Structured	5.1 / 1.5	3.885	3.962	0.761	0.552

Table 5: Timbre-editor training ablations on TimbreEdit-Bench. All post-training variants start from SFT (1M).

M

denotes the candidate count per condition; wDPO includes auxiliary SFT as defined in Equation 2 . SIM-T measures target-voice similarity; WER/CER reports English WER / Chinese CER in percent. Best and second-best scores within each instruction form are bold and underlined , with CER and WER ranked separately.

Configuration	Control (%)			Consistency	Content and quality
Configuration	SegAcc $\uparrow$	EmoAcc $\uparrow$	Trans $\uparrow$	SIM-I $\uparrow$	WER/CER (%) $\downarrow$	UTMOS $\uparrow$	DNSMOS $\uparrow$
Persistent KV cache	54.25	54.02	68.00	0.918	3.9 / 0.8	3.555	3.953
Truncated KV cache	58.05	56.70	64.94	0.892	4.4 / 1.1	3.278	3.905
EDICT-Stage II	82.30	83.60	82.00	0.915	5.1 / 1.8	3.631	3.930
w/o initial context	89.29	89.00	79.99	0.863	4.3 / 1.4	3.585	3.896
w/o recent context	81.15	82.26	55.32	0.875	4.2 / 1.1	3.548	3.830

Table 6: KV cache reconstruction ablations on IntraTTS-Bench. All variants use Qwen3-TTS-VD. The full configuration retains the first

S=4

and most recent

K=5

acoustic positions; the context ablations remove one of these groups. Persistent and truncated caches reuse old KV states, whereas EDICT recomputes them. Best and second-best scores are bold and underlined , with WER and CER ranked separately.

Appendix figures & tables20 assets

Supplementary material from the paper’s appendix.

Appendix

Task	Control input	Control target	Scope
Speech content editing	Recording + revised text	Spoken content	Selected span
Timbre editing	Reference + edit instruction	Selected timbre attributes	Utterance
Voice cloning	Reference speech	Voice identity	Utterance
Voice design	Description / attribute specification	Voice characteristics	Utterance
Global expressive control	Utterance-level instruction	Emotion, rate, prosody, etc	Utterance
Local expressive control	Span-specific instructions	Emotion, rate, prosody, etc	Word / segment

Appendix

Table 7: Taxonomy of speech control tasks. Typical control inputs, control targets, and temporal scope are shown. Inputs are not exhaustive, and tasks may overlap.

Method	Zero-shot cloning	Voice design	Timbre modification	Intra-utterance control	Control formulation
VoxInstruct ( Zhou et al., 2024 )	✓	✓	✗	✓ a	Instruction-guided synthesis and word-level emphasis
Qwen3-TTS-Base ( Hu et al., 2026a )	✓	✗	✗	✗	Reference-based voice cloning
Qwen3-TTS-VoiceDesign ( Hu et al., 2026a )	✓	✓	✗	✗	Description-guided voice design
Spark-TTS ( Wang et al., 2025b )	✓	✓	✗	✗	Attribute-guided design and reference cloning
VoiceSculptor ( Hu et al., 2026b )	✓	✓	✗	✗	Voice design followed by cloning
CosyVoice 2 ( Du et al., 2024 )	✓	✗	✗	✓ b	Instructed cloning and phrase-level tags

Appendix

Table 8: Comparison of speech generation and editing methods. Capabilities refer to the named models in the cited settings. Superscripts specify restricted scope or additional training.

Attribute	Values	Perceptual description
Perceived gender	Masculine, Feminine	Perceived vocal masculinity or femininity.
Age impression	Child, Young adult, Mature	Perceived age conveyed by the voice.
Pitch register	Low, Mid, High	Typical pitch range of the voice.
Brightness	Dark, Neutral, Bright	Perceived prominence of high-frequency energy.
Vocal weight	Thin, Medium, Thick	Perceived fullness and body of the voice.
Resonance focus	Head, Balanced, Chest	Resonance perceived as head- or chest-focused.

Appendix

Table 9: Perceptual attribute schema for timbre editing. Perceived gender and age impression are categorical; the seven remaining attributes use ordinal levels 1–3 in the listed order.

Figure 5: Synthetic speech-bank statistics. (a) Retained recording durations in 0.5-second bins. (b,c) Perceived gender and age impression. (d) Distributions of the seven ordinal attributes; levels 1–3 follow the schema in Table 9 . Profile percentages are normalized within the training (5,800) and held-out (200) subsets. Young denotes young adult.

Figure 6: Edit coverage across one million training pairs. (a) Number of changed attributes per pair. (b,c) Source-to-target transitions for perceived gender and age impression. (d) Signed target-minus-source differences for ordinal attributes; Keep denotes no change. Heatmaps use a shared color scale and show percentages of all pairs. Each complete matrix in (b,c) and each column in (d) sums to 100% before rounding. Young denotes young adult.

Figure 7: IntraTTS-Bench composition. (a,b) Request counts by segment count and topic, split by language. (c,d) Segment lengths in bins of width two, normalized within each language. Chinese lengths count non-whitespace characters, including punctuation; English lengths count whitespace-separated words.

Criterion	Valid annotations			Agreement (%)
Criterion	H1	H2	Gemini	H1–H2	H1–Gemini	H2–Gemini
SegAcc	197	198	200	90.4	83.8	85.9
EmoAcc	197	198	200	85.8	83.2	85.4
Trans	99	100	100	91.9	93.9	96.0

Appendix

Table 10: Human–Gemini agreement on intra-utterance control. Valid counts refer to segment-level judgments for SegAcc and EmoAcc, and boundary-level judgments for Trans. Agreement (%) uses jointly valid items for each pair.

Criterion	Valid annotations			Agreement (%)
Criterion	H1	H2	Gemini	H1–H2	H1–Gemini	H2–Gemini
Up	100	100	100	81.0	79.0	76.0
Down	100	100	100	73.0	67.0	66.0
Category	100	100	100	82.0	84.0	78.0
Preservation	100	100	100	74.0	72.0	70.0

Appendix

Table 11: Human–Gemini agreement on timbre editing. Each annotator provides 100 valid judgments per criterion. Agreement (%) is computed from paired pass/fail labels.

Figure 8: Instructions shown to listening-test participants. Panel (a) gives general guidance on playback and rating; panels (b) and (c) describe the timbre-editing and intra-utterance control tasks, respectively. System identities are hidden.

Figure 9: Listening-test interfaces. Panel (a) collects naturalness, edit accuracy, and target timbre similarity ratings in sequence; the target reference is revealed only after edit accuracy is rated. Panel (b) collects naturalness, instruction following, and transition quality ratings for the complete utterance. All criteria use five-point scales.

Figure 10: Attention across segment transitions. Rows show zero-indexed Transformer layers 7 and 14; columns compare TED-TTS and EDICT on the same request, using separate rollouts. We average attention over all 16 heads in each layer. $G$ denotes the global instruction, $I_{k}/\mathbf{x}_{k}$ the segment instruction/text, $H$ the preceding audio, $S/R$ the initial/recent context, and $\mathbf{c}_{2}$ the new segment’s audio positions. Gray cells are masked or unavailable. All panels share a power color scale with $\gamma=0.35$ .

Figure 11: Attention during KV cache reconstruction. Each layer group shows replay with (a) old KV states and old conditions, (b) reconstructed KV states and new conditions, and (c) the full prefill followed by decoding. The dashed coral box marks retained acoustic inputs whose KV states are recomputed; solid lines separate prefill from decoding. Layers 7 and 14 are zero-indexed, with attention averaged over all 16 heads. The replay comparison changes both cache and conditions, so it does not isolate reconstruction alone.

Figure 12: Statistics of the ESD evaluation set. (a) Speaker-pair coverage. (b) Number of edited attributes per request. (c) Attribute-change distributions. In-bar numbers indicate case counts.

System	Instruction	Content and quality			WavLM			ERes2Net
System	Instruction	WER/CER (%) $\downarrow$	UTMOS $\uparrow$	DNSMOS $\uparrow$	SIM-T $\uparrow$	SIM-R	$\Delta\uparrow$	SIM-T $\uparrow$	SIM-R	$\Delta\uparrow$
(a) English
Instruction-conditioned systems
Qwen3-TTS-VD	Structured	5.3	4.219	3.487	0.510	0.714	$\underline{-0.204}$	0.383	0.531	$\underline{-0.148}$
Qwen3-TTS-VD	Descriptive	6.1	4.162	3.400	0.483	0.709	$\underline{-0.226}$	0.385	0.533	$\underline{-0.148}$
Qwen3-TTS-Base	Structured	4.7	4.358	3.387	0.396	0.934	$-0.538$	0.301	0.770	$-0.469$

Appendix

Table 12: Objective results on the ESD evaluation set. WER/CER reports English WER or Mandarin CER. SIM-T/R denotes target/source similarity;

\Delta=\mathrm{SIM\text{-}T}-\mathrm{SIM\text{-}R}

. Best and second-best scores within each language and instruction form are bold and underlined ; SIM-R is unranked. Target-reference VC is shown separately, with its best similarity and

\Delta

scores in bold .

(a) SFT data scale
Configuration	Instruction	Content and quality			WavLM	ERes2Net
Configuration	Instruction	WER/CER (%) $\downarrow$	UTMOS $\uparrow$	DNSMOS $\uparrow$	SIM-T $\uparrow$	SIM-T $\uparrow$
SFT (0.5M)	Structured	3.9 / 1.5	3.689	3.895	0.736	0.498
SFT (0.5M)	Descriptive	4.2 / 1.5	3.708	3.885	0.698	0.481
SFT (1M)	Structured	4.1 / 0.9	3.936	3.896	0.757	0.561
SFT (1M)	Descriptive	3.9 / 0.9	3.666	3.913	0.693	0.556

Appendix

Table 13: Extended timbre-editor training ablations on TimbreEdit-Bench. SIM-T measures target-voice similarity; WER/CER reports English WER / Chinese CER in percent. Best and second-best scores within each instruction form are bold and underlined , with CER and WER ranked separately.

Figure 13: Sensitivity to the auxiliary SFT coefficient in wDPO on TimbreEdit-Bench. Solid and dashed curves denote structured and descriptive instructions, respectively. Shading marks the default

\lambda_{\mathrm{SFT}}=0.01

. The horizontal axis is linear up to

0.01

and logarithmic thereafter.

Initial context $S$	0	1	2	3	4	5
SIM-I $\uparrow$	0.872	0.885	0.902	0.899	0.912	0.915
EmoSim $\uparrow$	0.856	0.850	0.842	0.837	0.836	0.821

Appendix

Table 14: Effect of initial acoustic context on a 500-request IntraTTS-Bench subset. With Qwen3-TTS-VD and

K=5

, we vary the number

S

of initial acoustic positions retained during reconstruction. SIM-I measures speaker consistency; EmoSim measures emotion-embedding similarity to independently synthesized reference segments. Bold marks the default configuration, rather than the highest score.

Recent context $K$	1	2	3	4	5	6	7	8	9	10
SIM-I $\uparrow$	0.876	0.895	0.903	0.911	0.912	0.912	0.915	0.917	0.916	0.912
EmoSim $\uparrow$	0.837	0.842	0.838	0.832	0.836	0.831	0.825	0.819	0.821	0.817

Appendix

Table 15: Effect of recent acoustic context on a 500-request IntraTTS-Bench subset. With Qwen3-TTS-VD and

S=4

, we vary the number

K

of recent acoustic positions retained during reconstruction. SIM-I measures speaker consistency; EmoSim measures emotion-embedding similarity to independently synthesized reference segments. Bold marks the default configuration, rather than the highest score.

Language	System	WER/CER $\downarrow$	Emo2v $\uparrow$	SIM-I $\uparrow$	TCD $\downarrow$	OVRL $\uparrow$	UTMOS $\uparrow$
English	Concat.	1.18	88.33	0.667	0.467	3.799	3.550
	Joint	0.30	59.47	0.929	0.487	3.754	4.040
	TED-TTS	0.69	80.91	0.919	0.463	3.676	3.875
	EDICT-Stage II	1.09	85.81	0.932	0.357	3.739	3.863
Chinese	Concat.	1.74	85.56	0.589	0.488	3.665	3.528
	Joint	1.57	61.48	0.856	0.425	3.692	3.763

Appendix

Table 16: Intra-utterance emotion control on 500 MED-TTS examples. WER/CER denotes English WER or Chinese CER in percent. Emo2v is a segment-level emotion classification accuracy in percent; SIM-I measures inter-segment speaker similarity. TCD is reported as

10^{-3}\mathrm{TCD}

, and OVRL denotes DNSMOS OVRL. Best and second-best scores within each language are bold and underlined .

	Text category	System	WER/CER $\downarrow$	Emo2v $\uparrow$	SIM-I $\uparrow$	TCD $\downarrow$	OVRL $\uparrow$	UTMOS $\uparrow$
English	Emotional Dialogue	Concat.	2.93	90.18	0.659	0.452	3.807	3.462
		Joint	0.46	65.22	0.912	0.463	3.708	3.752
		TED-TTS	0.34	84.58	0.913	0.487	3.622	3.719
		EDICT-Stage II	1.23	88.58	0.927	0.342	3.699	3.750
	Observational Narrative	Concat.	0.43	84.62	0.652	0.469	3.820	3.525
		Joint	0.12	58.68	0.939	0.508	3.818	4.197

Appendix

Table 17: MED-TTS results by language and text category. WER/CER denotes English WER or Chinese CER in percent. Emo2v is a segment-level emotion classification accuracy in percent; SIM-I measures inter-segment speaker similarity. TCD is reported as

10^{-3}\mathrm{TCD}

, and OVRL denotes DNSMOS OVRL. Best and second-best scores within each language and category are bold and underlined .

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Speech editing for content creation requires precise control over both what an edit should do and where it should apply. Free-form natural language provides a flexible interface for expressing edit requests, but its ambiguity may leave the intended operation, parameters, or target region underspecified. We study a precise and explicit interface for speech editing: a transcript-grounded structural edit instruction with XML-style tags explicitly specifies typed operations and localizes them to transcript spans or boundaries. This semantic timeline avoids explicit timestamp alignment and provides an externally inspectable contract for compositional edits. We instantiate the interface in dots

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