Deterministic Prompting for Speaker-Stable Low-Resource Greek TTS

Abstract:Modern TTS systems approach human quality for high-resource languages but degrade when clean speech data is scarce. Modern Greek exemplifies this, lacking the curated corpora behind state-of-the-art synthesis. We propose a data curation recipe that transforms audiobook recordings into TTS-ready data via WhisperX alignment and filtering. Then we fine-tune Parler-TTS (880M), a prompt-based multilingual model whose pre-training encodes phonetic priors transferable to Greek. During development, we find that LLM-generated style prompts introduce speaker drift at inference. Replacing them with deterministic prompts resolves this, and a speaker-specific LoRA stage trained on 3.5 h of single-speaker data anchors identity while updating ~5% of parameters. Our system achieves WER 10.7% (2.9 above the ASR floor), MOS-I 4.00 (vs. 4.36 human speech), and near-human speaker consistency (MOS-C 4.24 vs. 4.30), showing that robust single-speaker Greek TTS is achievable with limited curated data.

Sound (cs.SD); Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.10022 [cs.SD]

(or arXiv:2609.10022v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2609.10022 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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