E3-System: Expressive EmoData, Explicitly Controllable, Easy-to-Use TTS
Organizations: Harvard University, Cambridge, MA, USA · Baidu Inc., Shenzhen, China · The Chinese University of Hong Kong, Shenzhen, China
Abstract
Recent zero-shot Text-to-Speech (TTS) systems can clone unseen speakers, yet controllable expressiveness remains limited by a gap between semantic intent and acoustic realization. Text descriptions offer a natural control interface but provide weak acoustic grounding, whereas speech style prompts provide concrete prosody but entangle style with the prompt speaker's timbre. To bridge this gap, we propose E3-System, a unified intent-to-speech framework for expressive zero-shot TTS that couples three requirements: Expressive style modeling, Explicit timbre-style control, and Easy-to-use natural-language interaction. Given a target speaker and either a natural-language style description or a speech style prompt, E3-System generates expressive speech while preserving the target timbre. The key idea is to close the full control loop: building a large expressive style space, grounding user intent into usable acoustic style references, and synthesizing speech with disentangled timbre and style. To realize this loop, EmoData provides a thousand-hour multilingual emotional speech dataset, Style-RAG grounds text descriptions into high-quality acoustic style prompts from these data, and Dual-TTS transfers the grounded style to arbitrary target speakers through VC-augmented timbre-style disentanglement. Experiments show that E3-System achieves strong speech quality, speaker preservation, and explicit expressive controllability.
Figures & tables
| Dataset | SIM-Spk | SIM-Style |
|---|---|---|
| Real Speaker GT | 82.50 | 71.20 |
| Our Pseudo GT | 77.60 | 92.85 |
| Dataset | Language Num | Num Speakers | Dur (hrs) | SR (kHz) | Wild/ Studio |
|---|---|---|---|---|---|
| IEMOCAP [ 10 ] | EN | 10 | 12.6 | 48 | S |
| EMOV-DB [ 11 ] | EN,FR | 5 | 7.2 (E.) | 16 | S |
| ESD [ 14 ] | EN,ZH | 20 | 30 (E.) | 16 | S |
| KazEmo [ 13 ] | KA | 3 | 74.85 | 44.1 | S |
| JVNV [ 12 ] | JA | 4 | 3.94 | 44.1 | S |
| EmoData | EN,ZH,JA, KO,GE,FR | 5681 | 1012 | 44.1 | W |
| Neutral | Happy | Angry | Sad | Surprised | Disgusted |
| 95.20 | 91.75 | 92.30 | 90.45 | 85.10 | 78.65 |
| Dataset | WER(%) | SIM-Spk | SIM-Style |
|---|---|---|---|
| CosyVoice [ 1 ] | 3.05 | 70.68 | 72.51 |
| Finetuned w/ Raw | 3.21 | 71.33 | 71.21 |
| Finetuned w/ Ours | 2.98 | 73.04 | 78.63 |
| Model | Wav P. | Txt P. | WER | SIM | SS-MOS | SN-MOS |
|---|---|---|---|---|---|---|
| Text-Prompted TTS (w/o Zero-Shot Voice Cloning) | ||||||
| PromptTTS [ 23 ] | ✗ | ✓ | 4.25 | NA | NA | 3.51 0.12 |
| InstructTTS [ 24 ] | ✗ | ✓ | 6.78 | NA | NA | 3.47 0.13 |
| Zero-Shot TTS (w/o Disentangled Style) | ||||||
| FireRedTTS [ 18 ] | ✗ | ✗ | 3.79 | 60.02 | 3.51 0.09 | 3.72 0.10 |
| MaskGCT [ 19 ] | ✗ | ✗ | 2.63 | 69.70 | 3.75 0.16 | 3.74 0.12 |
| Model | WER | SIM- Spk | SIM- Style | SM-MOS | SN-MOS |
|---|---|---|---|---|---|
| Vevo-TTS [ 3 ] | 3.52 | 65.16 | 85.06 | 3.90 0.15 | 3.87 0.21 |
| IndexTTS2 [ 4 ] | 2.63 | 69.85 | 86.73 | 3.96 0.13 | 3.88 0.13 |
| DisCo [ 5 ] | 3.16 | 63.68 | 87.25 | 4.00 0.11 | 3.91 0.09 |
| Ours (GRL) | 3.08 | 59.02 | 72.50 | 3.61 0.13 | 3.74 0.12 |
| Ours w/o RAG | 2.51 | 70.15 | 89.22 | 4.08 0.14 | 3.98 0.11 |
| Ours w/ RAG | 2.93 | 69.08 | 86.33 | 3.97 0.12 | 3.96 0.10 |
TTS Synthesis
Text-to-speech synthesis converts written language into intelligible, natural-sounding speech with control over voice, prosody, style, and timing.
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