Zero-Shot vs. Few-Shot Multi-Speaker TTS Using Pre-trained Czech SpeechT5 Model
Sound
2024-09-26 v1 Computation and Language
Audio and Speech Processing
Abstract
In this paper, we experimented with the SpeechT5 model pre-trained on large-scale datasets. We pre-trained the foundation model from scratch and fine-tuned it on a large-scale robust multi-speaker text-to-speech (TTS) task. We tested the model capabilities in a zero- and few-shot scenario. Based on two listening tests, we evaluated the synthetic audio quality and the similarity of how synthetic voices resemble real voices. Our results showed that the SpeechT5 model can generate a synthetic voice for any speaker using only one minute of the target speaker's data. We successfully demonstrated the high quality and similarity of our synthetic voices on publicly known Czech politicians and celebrities.
Cite
@article{arxiv.2407.17167,
title = {Zero-Shot vs. Few-Shot Multi-Speaker TTS Using Pre-trained Czech SpeechT5 Model},
author = {Jan Lehečka and Zdeněk Hanzlíček and Jindřich Matoušek and Daniel Tihelka},
journal= {arXiv preprint arXiv:2407.17167},
year = {2024}
}
Comments
Accepted to TSD2024