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This research addresses the data scarcity problem in direction-following text-to-speech by introducing a scalable pipeline to synthesize pseudo-triplets for training. By leveraging an impression-controllable model and an LLM to generate natural language directions, the method achieves stable speaker preservation while aligning with specific performance instructions. The approach demonstrates that synthetic data can effectively augment real recordings to improve style variation without compromising identity.
Scalable Direction-Following TTS via Voice Impression-Guided Pseudo Triplet Construction
arXiv:2609.02623v1 Announce Type: cross Abstract: Voice actors often re-read the same script while modifying their delivery in response to performance directions. We study this setting as direction-following TTS, where a system generates a new utterance that reflects a given direction relative to a reference utterance while preserving speaker identity and linguistic content. A key challenge is the lack of training data capturing such relative modifications. To address this, we propose a scalable pseudo-triplet construction pipeline that generates~(reference utterance, direction text, modified utterance) triplets. It generates controlled style variations using an impression-controllable TTS model and uses an LLM to produce natural language directions from estimated impression differences. Experimental results demonstrate that pseudo-triplets alone enable stable speaker-preserving modification, and that combining pseudo and recorded data further improves direction alignment while maintaining speaker similarity. Audio examples are available on our demo page https://ntt-hilab-gensp.github.io/IS2026pseudo/
Abstract:Voice actors often re-read the same script while modifying their delivery in response to performance directions. We study this setting as direction-following TTS, where a system generates a new utterance that reflects a given direction relative to a reference utterance while preserving speaker identity and linguistic content. A key challenge is the lack of training data capturing such relative modifications. To address this, we propose a scalable pseudo-triplet construction pipeline that generates~(reference utterance, direction text, modified utterance) triplets. It generates controlled style variations using an impression-controllable TTS model and uses an LLM to produce natural language directions from estimated impression differences. Experimental results demonstrate that pseudo-triplets alone enable stable speaker-preserving modification, and that combining pseudo and recorded data further improves direction alignment while maintaining speaker similarity. Audio examples are available on our demo page this https URL
Sound (cs.SD); Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2609.02623 [cs.SD]
(or arXiv:2609.02623v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2609.02623 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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