# LoRP-TTS: Low-Rank Personalized Text-To-Speech

arXiv: https://arxiv.org/abs/2502.07562 · HTML: https://mlsanity.com/paper/2502.07562/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [UtterTune: LoRA-Based Target-Language Pronunciation Edit and Control in Multilingual Text-to-Speech](https://mlsanity.com/paper/2508.09767/) (2025)
- [Fed-PISA: Federated Voice Cloning via Personalized Identity-Style Adaptation](https://mlsanity.com/paper/2509.16010/) (2025)
- [When Fine-Tuning Fails and when it Generalises: Role of Data Diversity and Mixed Training in LLM-based TTS](https://mlsanity.com/paper/2603.10904/) (2026)

## Abstract (arXiv)

Speech synthesis models convert written text into natural-sounding audio. While earlier models were limited to a single speaker, recent advancements have led to the development of zero-shot systems that generate realistic speech from a wide range of speakers using their voices as additional prompts. However, they still struggle with imitating non-studio-quality samples that differ significantly from the training datasets. In this work, we demonstrate that utilizing Low-Rank Adaptation (LoRA) allows us to successfully use even single recordings of spontaneous speech in noisy environments as prompts. This approach enhances speaker similarity by up to 30pp while preserving content and naturalness. It represents a significant step toward creating truly diverse speech corpora, that is crucial in all speech-related tasks.