Tibetan-TTS:Low-Resource Tibetan Speech Synthesis with Large Model Adaptation
Abstract
Tibetan text-to-speech (TTS) has long been challenged by scarce speech resources, significant dialectal variation, and the complex mapping between written text and spoken pronunciation. To address these issues, this work presents, to the best of our knowledge, the first large-model-based Tibetan TTS system in the industry, built upon a large speech synthesis model developed by Xingchen AGI Lab. The proposed system integrates data quality enhancement, Tibetan-oriented text representation and tokenizer adaptation, and cross-lingual adaptive training for low-resource Tibetan speech synthesis. Experimental results show that the system can generate stable, natural, and intelligible Tibetan speech under low-resource conditions. In subjective evaluation, the MOS scores of the syllable-level and BPE-based systems reach 4.28 and 4.35, while their pronunciation accuracies reach 97.6% and 96.6%, respectively, outperforming an external commercial Tibetan TTS interface. These results demonstrate that combining a large-model backbone with Tibetan-oriented text representation adaptation and cross-lingual adaptive training enables highly usable low-resource Tibetan speech synthesis, and also provides a technical foundation for future unified multi-dialect Tibetan speech synthesis.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper establishes a pioneering framework for low-resource Tibetan speech synthesis, combining innovative methodologies with practical applications. The integration of data quality enhancement, tailored text representation, and cross-lingual training marks a significant advancement in the field of speech synthesis, particularly for minority languages facing resource constraints.
Methodology
The paper presents a comprehensive approach to Tibetan TTS by integrating a data quality enhancement pipeline, a Tibetan-oriented text representation, and a cross-lingual adaptive training strategy. The methodology is well-structured, addressing the unique challenges of Tibetan speech synthesis, such as dialectal variation and resource scarcity. The use of a large-model backbone and the adaptation of tokenization strategies specifically for Tibetan linguistic characteristics are notable innovations that enhance the model's performance under low-resource conditions.
Results & evaluation
The experiments are robust, with both subjective (MOS scores) and objective (pronunciation accuracy) evaluations demonstrating the effectiveness of the proposed system. The reported MOS scores of 4.28 and 4.35 for the syllable-level and BPE-based systems, respectively, indicate a high level of naturalness and intelligibility. The comparison with an external commercial TTS interface further validates the system's performance, showcasing its potential for practical applications.
Reproducibility
The paper lacks detailed implementation specifics, such as code availability or dataset access, which could hinder reproducibility. While the methodology is clearly described, the absence of a public repository or demo limits the ability for other researchers to replicate the results.
Limitations
The study primarily focuses on the U-Tsang dialect, which may limit the generalizability of the findings to other Tibetan dialects. Additionally, while the proposed methods show promise, the reliance on a large pretrained model may not be feasible in all low-resource scenarios, particularly where such models are not available.
Broader impact
The development of a Tibetan TTS system has significant implications for cultural preservation, education, and accessibility in Tibetan-speaking regions. By providing a framework for low-resource language synthesis, this work could serve as a model for similar efforts in other underrepresented languages, promoting linguistic diversity in technology. This paper establishes a pioneering framework for low-resource Tibetan speech synthesis, combining innovative methodologies with practical applications. The integration of data quality enhancement, tailored text representation, and cross-lingual training marks a significant advancement in the field of speech synthesis, particularly for minority languages facing resource constraints.
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