Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models
This paper proposes novel techniques for inter-utterance style interpolation and intra-utterance style transition in prompt-based TTS models, addressing limitations of coarse global control. Methods include direction vector interpolation and KV-cache swapping with sliding-window attention masking. Experiments show high success rates in gender conversion and smooth style transitions within utterances.
Article intelligence
Key points
- Inter-utterance interpolation via direction vectors between contrastive style prompts enables smooth transitions.
- Intra-utterance transition uses KV-cache swapping and sliding-window masking to overcome attention bias.
- Achieves 99-100% gender conversion success, up to 36 Hz pitch variation, and up to 1.6 syllables/second speed change.
- Intra-utterance method maintains speaker similarity of 0.81-0.91 and perceptual smoothness scores of 3.48-4.48.
Why it matters
This matters because inter-utterance interpolation via direction vectors between contrastive style prompts enables smooth transitions.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27376] Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models
[Submitted on 9 Apr 2026]
Title:Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models
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Abstract:While prompt-based text-to-speech (TTS) models enable natural language-driven speaking style control, they often provide limited fine-grained control and apply a single global style across an utterance. This restricts practical use cases that require continuous style attribute interpolation across utterances and time-varying style transitions within a single utterance. In this paper, we propose novel techniques to achieve both capabilities in existing prompt-based TTS models. For inter-utterance style interpolation, we compute direction vectors between contrastive style prompts in the embedding space and perform simple interpolation, enabling smooth transitions between style characteristics. For intra-utterance style transition, we first identify a strong attention bias toward early tokens in autoregressive TTS decoders, causing the initial audio realization to dominate subsequent generation. To mitigate this effect, we introduce KV-cache swapping and sliding-window attention masking. Experiments demonstrate that our proposed inter-utterance interpolation achieves a 99-100% success rate in gender conversion, up to 36 Hz pitch variation, and up to 1.6 syllables-per-second speed change. Our intra-utterance transition maintains a speaker similarity of 0.81-0.91 and achieves perceptual smoothness scores of 3.48-4.48.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.27376 [cs.CL]
(or arXiv:2605.27376v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.27376
arXiv-issued DOI via DataCite
Submission history
From: Jaehoon Kang [view email] [v1] Thu, 9 Apr 2026 01:06:26 UTC (1,394 KB)
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