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New TTS method achieves speaker-stable Greek synthesis with limited data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method for creating high-quality Text-to-Speech (TTS) for low-resource languages, specifically focusing on Modern Greek. Their approach involves curating audiobook data using WhisperX for alignment and filtering, then fine-tuning the Parler-TTS model. To address speaker drift issues caused by LLM-generated prompts, they implemented deterministic prompts and a LoRA stage for speaker identity, achieving a Word Error Rate of 10.7% and a Mean Opinion Score for Intelligibility of 4.00. AI

IMPACT Demonstrates a viable approach for developing high-quality TTS in languages with scarce data, potentially improving accessibility.

RANK_REASON Academic paper detailing a new methodology for TTS in a low-resource language. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New TTS method achieves speaker-stable Greek synthesis with limited data

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Academic paper detailing a new methodology for TTS in a low-resource language. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Georgios Syllas, Efthymios Georgiou, Kosmas Kritsis, Alexandros Potamianos ·

    Deterministic Prompting for Speaker-Stable Low-Resource Greek TTS

    arXiv:2609.10022v1 Announce Type: cross 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 cu…

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