Zero-Shot vs Few-Shot Multi-Speaker TTS Using Pre-trained Czech SpeechT5 Model
<|reference_start|>Zero-Shot vs Few-Shot Multi-Speaker TTS Using Pre-trained Czech SpeechT5 Model: 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.<|reference_end|>
@article{lehečka2024zero-shot,
    title={Zero-Shot vs. Few-Shot Multi-Speaker TTS Using Pre-trained Czech
  SpeechT5 Model},
    author={Jan Lehev{c}ka, Zdenv{e}k Hanzl'iv{c}ek, Jindv{r}ich
  Matouv{s}ek, Daniel Tihelka},
    journal={Text, Speech, and Dialogue. TSD 2024. Lecture Notes in Computer
  Science(), vol 15049},
    year={2024},
    doi={10.1007/978-3-031-70566-3_5},
    archivePrefix={arXiv},
    eprint={2407.17167},
    primaryClass={cs.SD cs.CL eess.AS}
}