E3-System: Expressive EmoData, Explicitly Controllable, Easy-to-Use TTS
On the map: Speech and audio language models → Emotional speech synthesis and recognition also fits: Vocoders and speech synthesis
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 19 Oct 2026). Placed in Emotional speech synthesis and recognition, one of 11 directions in Speech and audio language models. Builds on 32 of its 35 references that are in the map. First posted on arXiv 9 Oct 2026.
Builds on
32 of 35 references.
Closest ancestors · 4
Same direction, newest first
- 2025IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech23 Jun
- 2024KazEmoTTS: A Dataset for Kazakh Emotional Text-to-Speech Synthesis1 Apr
- 2023JVNV: A Corpus of Japanese Emotional Speech with Verbal Content and Nonverbal Expressions9 Oct
- 2022iEmoTTS: Toward Robust Cross-Speaker Emotion Transfer and Control for Speech Synthesis based on Disentanglement between Prosody and Timbre29 Jun
Other references · 28
- 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 2,135
- 2023Neural Codec Language Models are Zero-Shot Text to Speech Synthesizerscited by 1,007
- 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 864
- 2022Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentationcited by 772
- 2024CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokenscited by 456
- 2024Seed-TTS: A Family of High-Quality Versatile Speech Generation Modelscited by 399
- 2024CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Modelscited by 397
- 2024NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Modelscited by 316
- 2024Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generationcited by 268
- 2023emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representationcited by 263
- 2024MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformercited by 232
- 2025Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokenscited by 162
16 more
- 2022PromptTTS: Controllable Text-to-Speech with Text Descriptionscited by 157
- 2024FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMscited by 156
- 2023SoundStorm: Efficient Parallel Audio Generationcited by 154
- 2021Emotional Voice Conversion: Theory, Databases and ESDcited by 146
- 2023InstructTTS: Modelling Expressive TTS in Discrete Latent Space with Natural Language Style Promptcited by 139
- 2024FireRedTTS: A Foundation Text-To-Speech Framework for Industry-Level Generative Speech Applicationscited by 106
- 2018The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systemscited by 73
- 2023PromptTTS 2: Describing and Generating Voices with Text Promptcited by 68
- 2024WenetSpeech4TTS: A 12,800-hour Mandarin TTS Corpus for Large Speech Generation Model Benchmarkcited by 54
- 2025Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised Disentanglementcited by 52
- 2025OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academiacited by 23
- 2023MM-TTS: Multi-modal Prompt based Style Transfer for Expressive Text-to-Speech Synthesiscited by 17
- 2025HiFiTTS-2: A Large-Scale High Bandwidth Speech Datasetcited by 13
- 2025AutoStyle-TTS: Retrieval-Augmented Generation based Automatic Style Matching Text-to-Speech Synthesiscited by 4
- 2025DisCo-Speech: Controllable Zero-Shot Speech Generation with A Disentangled Speech Codeccited by 4
- 2024Debatts: Zero-Shot Debating Text-to-Speech Synthesiscited by 3
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026FC-TTS: Style and Timbre Control in Zero-Shot Text-to-Speech with Disentangled Speech Representations
- 2023EE-TTS: Emphatic Expressive TTS with Linguistic Information
- 2024StyleFusion TTS: Multimodal Style-control and Enhanced Feature Fusion for Zero-shot Text-to-speech Synthesis
- 2024Expressive Prompting: Improving Emotion Intensity and Speaker Consistency in Zero-Shot TTS
- 2026ReStyle-TTS: Relative and Continuous Style Control for Zero-Shot Speech Synthesis
- 2022Text-driven Emotional Style Control and Cross-speaker Style Transfer in Neural TTS
- 2026EmoTra-TTS: Smooth Intra-Utterance Emotion Transitions for Speech Synthesis
- 2022Contextual Expressive Text-to-Speech