JaiTTS: A Thai Voice Cloning Model

Jullajak Karnjanaekarin; Pontakorn Trakuekul; Narongkorn Panitsrisit; Sumana Sumanakul; Vichayuth Nitayasomboon; Nithid Guntasin; Thanavin Denkavin; Attapol T. Rutherford

cs.CLApr 30, 2026

JaiTTS: A Thai Voice Cloning Model

Authors: Jullajak Karnjanaekarin, Pontakorn Trakuekul, Narongkorn Panitsrisit, Sumana Sumanakul, Vichayuth Nitayasomboon, Nithid Guntasin, Thanavin Denkavin, Attapol T. Rutherford

Organizations: Jasmine Technology Solution · Sirindhorn International Institute of Technology · Department of Linguistics, Chulalongkorn University

Abstract

We present JaiTTS-v1.0, a state-of-the-art Thai voice cloning text-to-speech model built through continual training on a large Thai-centric speech corpus. The model architecture is adapted from VoxCPM, a tokenizer-free autoregressive TTS model. JaiTTS-v1.0 directly processes numerals and Thai-English code-switching, which is very common in realistic settings, without explicit text normalization. We test the models on short- and long-duration speech generation, which reflects many real-world use cases. JaiTTS-v1.0 achieves a state-of-the-art CER of 1.94%, surpassing the human ground truth of 1.98% for short-duration tasks while performing on par with human ground truth for long-duration tasks. In human judgment evaluations, our model wins 283 of 400 pairwise comparisons against commercial flagships, with only 58 losses. Our code and demo are available at https://github.com/JTS-AI-Team/JaiTTS .

Explore similar work

Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech
Sep 3, 2026Kunat Pipatanakul, Potsawee Manakul, Warit Sirichotedumrong +3Cross-Lingual Voice Cloning Prosody
Taming Long-form Text-to-Speech
Sep 15, 2026Rongxiang Wang, Berkin Durmus, Aysegul Orhon +2Word Error Rate
X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning
May 7, 2026Rixi Xu, Qingyu Liu, Haitao Li +10Cross-Lingual Voice Cloning Multilingual Automatic Speech Recognition

Sep 3, 2026 · cs.CLJ/K move · Enter open · S save

Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech

Kunat Pipatanakul, Potsawee Manakul, Warit Sirichotedumrong, Sittipong Sripaisarnmongkol+2

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 large voice-cloning model as a programmable data source to turn a short voice reference (e.g., 15 seconds) into a compact fixed-voice student trained entirely on synthetic speech. This setting makes pipeline design consequential: teacher errors become training targets, while filtering failed generations can reduce coverage of difficult texts. Thai further introduces challenges from ambiguous word boundaries, lexical tone, names and loanwords, numeric verbalization, and Thai-English code-switching. We study how text preparation, synthetic generation, quality filtering, rejection sampling, and frontend choices affect the resulting student, and where teacher limitations remain. We evaluate CER, Challenge-Set Keyword Accuracy, Prosody Pause Accuracy, speaker similarity, and speaking rate. The resulting 82M-parameter model, Wayu-Paxa-TTS-Edge, enables on-device Thai TTS without reference audio. It achieves 68.2% Challenge-Set Keyword Accuracy (85.5% of Gemini 3.1) and 91.4% pause precision, outperforming its OmniVoice teacher (89.9%) and reaching 94.8% of Gemini 3.1. It also achieves the lowest pause-placement error and intra-word pause rates among the three systems, and 3.7% and 1.1% CER on Thai and English, respectively. We open-source the model and evaluation framework for Thai TTS development.

Cross-Lingual Voice Cloning Prosody

Open paper Ask about it PDF

Abstract

Explore similar work

Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech

Taming Long-form Text-to-Speech

X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning