Greek TTS from 3.5h Data: Deterministic Prompts Fix Speaker Drift, Near-Human MOS
kastnerkyle · x · 2026-09-13
An Interspeech 2026 paper shows how to build high-quality single-speaker TTS for a low-resource language.
- Data curation: WhisperX alignment and filtering turns audiobook recordings into TTS-ready data
- Base model: fine-tuned Parler-TTS (880M), whose pre-training encodes phonetic priors transferable to Greek
- Key finding: LLM-generated style prompts cause speaker drift at inference; replacing them with deterministic prompts resolves it
- LoRA anchoring: a speaker-specific LoRA trained on just 3.5h of single-speaker data (5% of parameters) anchors identity
- Results: WER 10.7% (2.9 above the ASR floor), MOS-I 4.00 vs 4.36 for human speech, speaker consistency MOS-C 4.24 vs 4.30
Conclusion: robust single-speaker Greek TTS is achievable with limited curated data.
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