基于音频的语言学特征提取用于增强多语言和低资源文本转语音
Audio-Based Linguistic Feature Extraction for Enhancing Multi-lingual and Low-Resource Text-to-Speech
AI总结：
针对多语言低资源文本转语音中语言表征学习不足的问题，提出直接从音频提取语言学特征并过滤声学信息的新方法，实验证明其在多语言合成和未见语言迁移学习中表现优越。
摘要：
获取大量高质量数据的困难，尤其是在多语言语境中，引发了对解决低资源场景的兴趣。此外，当前文献依赖语言ID的固定表达，导致语言表征学习不充分，且无法生成未见语言的语音。为应对这些挑战，我们提出一种新方法，直接从音频输入中提取语言学特征，同时有效过滤包括音色等说话者特定属性在内的杂项声学信息。主观和客观评估证实了我们的方法在多语言文本转语音中的有效性，并突显了其在未见语言低资源迁移学习中的优越性。
英文摘要：
The difficulty of acquiring abundant, high-quality data, especially in multi-lingual contexts, has sparked interest in addressing low-resource scenarios. Moreover, current literature rely on fixed expressions from language IDs, which results in the inadequate learning of language representations, and the failure to generate speech in unseen languages. To address these challenges, we propose a novel method that directly extracts linguistic features from audio input while effectively filtering out miscellaneous acoustic information including speaker-specific attributes like timbre. Subjective and objective evaluations affirm the effectiveness of our approach for multi-lingual text-to-speech, and highlight its superiority in low-resource transfer learning for previously unseen language.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
