DrawSpeech: Expressive Speech Synthesis Using Prosodic Sketches as Control Conditions
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Abstract (arXiv)
Controlling text-to-speech (TTS) systems to synthesize speech with the prosodic characteristics expected by users has attracted much attention. To achieve controllability, current studies focus on two main directions: (1) using reference speech as prosody prompt to guide speech synthesis, and (2) using natural language descriptions to control the generation process. However, finding reference speech that exactly contains the prosody that users want to synthesize takes a lot of effort. Description-based guidance in TTS systems can only determine the overall prosody, which has difficulty in achieving fine-grained prosody control over the synthesized speech. In this paper, we propose DrawSpeech, a sketch-conditioned diffusion model capable of generating speech based on any prosody sketches drawn by users. Specifically, the prosody sketches are fed to DrawSpeech to provide a rough indication of the expected prosody trends. DrawSpeech then recovers the detailed pitch and energy contours based on the coarse sketches and synthesizes the desired speech. Experimental results show that DrawSpeech can generate speech with a wide variety of prosody and can precisely control the fine-grained prosody in a user-friendly manner. Our implementation and audio samples are publicly available.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jan 2025.
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Citing papers per year: 2025: 5; 2026: 1.
6 papers.
- 2025IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speechcited by 90
- 2025ProsodyLM: Uncovering the Emerging Prosody Processing Capabilities in Speech Language Modelscited by 4
- 2025CoMelSinger: Discrete Token-Based Zero-Shot Singing Synthesis With Structured Melody Control and Guidancecited by 3
- 2025Task Vector in TTS: Toward Emotionally Expressive Dialectal Speech Synthesiscited by 3
- 2025Improving French Synthetic Speech Quality via SSML Prosody Controlcited by 1
- 2026Imitation Learning for Elder-Facing Speech Synthesiscited by 0
- 2026Imitation Learning for Elder-Facing Speech Synthesis19 Jun 2026
- 2025Task Vector in TTS: Toward Emotionally Expressive Dialectal Speech Synthesis21 Dec 2025
- 2025CoMelSinger: Discrete Token-Based Zero-Shot Singing Synthesis With Structured Melody Control and Guidance24 Sep 2025
- 2025Improving French Synthetic Speech Quality via SSML Prosody Control24 Aug 2025
- 2025ProsodyLM: Uncovering the Emerging Prosody Processing Capabilities in Speech Language Models27 Jul 2025
- 2025IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech23 Jun 2025
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- 2025Investigating Stochastic Methods for Prosody Modeling in Speech Synthesis
- 2024Towards Expressive Zero-Shot Speech Synthesis with Hierarchical Prosody Modeling
- 2024ProsodyFM: Unsupervised Phrasing and Intonation Control for Intelligible Speech Synthesis
- 2023High-Fidelity Speech Synthesis with Minimal Supervision: All Using Diffusion Models
- 2023DiffProsody: Diffusion-based Latent Prosody Generation for Expressive Speech Synthesis with Prosody Conditional Adversarial Training
- 2025DiffCSS: Diverse and Expressive Conversational Speech Synthesis with Diffusion Models
- 2022NoreSpeech: Knowledge Distillation based Conditional Diffusion Model for Noise-robust Expressive TTS
- 2021On the Interplay Between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis