# Audio-Based Linguistic Feature Extraction for Enhancing Multi-lingual and Low-Resource Text-to-Speech

arXiv: https://arxiv.org/abs/2409.18622 · HTML: https://mlsanity.com/paper/2409.18622/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [OpenBibleTTS: Large-Scale Speech Resources and TTS Models for Low-Resource Languages](https://mlsanity.com/paper/2606.09553/) (2026)

## Abstract (arXiv)

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.