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Towards Controllable Speech Synthesis in the Era of Large Language Models: A Systematic Survey

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arxiv 2412.06602 v3 pith:5YBVB564 submitted 2024-12-09 cs.CL cs.AIcs.LGcs.MMcs.SDeess.AS

Tianxin Xie , Yan Rong , Pengfei Zhang , Wenwu Wang , Li Liu This is my paper
classification cs.CLcs.AIcs.LGcs.MMcs.SDeess.AS
keywords controllablecontrollanguagesurveycomprehensivelargemodelsspeech
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Text-to-speech (TTS) has advanced from generating natural-sounding speech to enabling fine-grained control over attributes like emotion, timbre, and style. Driven by rising industrial demand and breakthroughs in deep learning, e.g., diffusion and large language models (LLMs), controllable TTS has become a rapidly growing research area. This survey provides the first comprehensive review of controllable TTS methods, from traditional control techniques to emerging approaches using natural language prompts. We categorize model architectures, control strategies, and feature representations, while also summarizing challenges, datasets, and evaluations in controllable TTS. This survey aims to guide researchers and practitioners by offering a clear taxonomy and highlighting future directions in this fast-evolving field. One can visit https://github.com/imxtx/awesome-controllabe-speech-synthesis for a comprehensive paper list and updates.

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Cited by 5 Pith papers

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  2. TokenChain: A Discrete Speech Chain via Semantic Token Modeling

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    TokenChain demonstrates that a discrete semantic-token interface can sustain effective chain learning between ASR and TTS, yielding faster convergence and lower error rates on LibriSpeech and TED-LIUM.

  3. FC-TTS: Style and Timbre Control in Zero-Shot Text-to-Speech with Disentangled Speech Representations

    eess.AS 2026-05 unverdicted novelty 6.0 of 10

    FC-TTS presents a zero-shot TTS framework that integrates disentangled speech representations with architectural choices, training framework, and auxiliary objectives to enable independent style and timbre control fro...

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  5. Position: Towards Responsible Evaluation for Text-to-Speech

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