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EmoVoice: LLM-based Emotional Text-To-Speech Model with Freestyle Text Prompting
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Human speech goes beyond the mere transfer of information; it is a profound exchange of emotions and a connection between individuals. While Text-to-Speech (TTS) models have made huge progress, they still face challenges in controlling the emotional expression in the generated speech. In this work, we propose EmoVoice, a novel emotion-controllable TTS model that exploits large language models (LLMs) to enable fine-grained freestyle natural language emotion control, and a phoneme boost variant design that makes the model output phoneme tokens and audio tokens in parallel to enhance content consistency, inspired by chain-of-thought (CoT) and chain-of-modality (CoM) techniques. Besides, we introduce EmoVoice-DB, a high-quality 40-hour English emotion dataset featuring expressive speech and fine-grained emotion labels with natural language descriptions. EmoVoice achieves state-of-the-art performance on the English EmoVoice-DB test set using only synthetic training data, and on the Chinese Secap test set using our in-house data. We further investigate the reliability of existing emotion evaluation metrics and their alignment with human perceptual preferences, and explore using SOTA multimodal LLMs GPT-4o-audio and Gemini to assess emotional speech. Dataset, code, checkpoints, and demo samples are available at https://github.com/yanghaha0908/EmoVoice.
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Cited by 6 Pith papers
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EmoInstruct-TTS: Dual-Path Instruction-Guided Emotional Speech Synthesis
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Enhancing Conversational TTS with Cascaded Prompting and ICL-Based Online Reinforcement Learning
A cascaded audio-prompting and ICL-based online RL method improves naturalness and expressivity in conversational TTS with reduced data needs.
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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts
MoE-TTS adds frozen text-expert MoE modules to a Qwen3-based TTS system and reports better out-of-domain description alignment than ElevenLabs and MiniMax on a small hand-built test set.
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