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New Thai TTS model trained on synthetic speech for low-resource settings

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method for creating compact, fixed-voice Text-to-Speech (TTS) systems for low-resource languages like Thai. This approach utilizes a large voice-cloning model as a data generator to train a smaller, on-device model using only synthetic speech, bypassing the need for extensive speaker-specific audio data. The resulting Wayu-Paxa-TTS-Edge model demonstrates strong performance in keyword accuracy and pause precision, outperforming its teacher model and approaching the capabilities of larger systems like Gemini 3.1, while also being open-sourced. AI

IMPACT Enables on-device TTS for low-resource languages by leveraging synthetic data, potentially accelerating global AI accessibility.

RANK_REASON The cluster describes an academic paper detailing a new method for TTS model development and evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New Thai TTS model trained on synthetic speech for low-resource settings

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The cluster describes an academic paper detailing a new method for TTS model development and evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kunat Pipatanakul, Potsawee Manakul, Warit Sirichotedumrong, Sittipong Sripaisarnmongkol, Pakorn Nathong, Phatrasek Jirabovonvisut ·

    Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech

    arXiv:2609.03502v1 Announce Type: cross Abstract: In low-resource settings, deploying TTS typically requires choosing between a large voice-cloning model with costly inference or a compact fixed-voice system that requires a speaker-specific corpus. We study a third route: using a…

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