DualSpeech: Enhancing Speaker-Fidelity and Text-Intelligibility Through Dual Classifier-Free Guidance
<|reference_start|>DualSpeech: Enhancing Speaker-Fidelity and Text-Intelligibility Through Dual Classifier-Free Guidance: Text-to-Speech (TTS) models have advanced significantly, aiming to accurately replicate human speech's diversity, including unique speaker identities and linguistic nuances. Despite these advancements, achieving an optimal balance between speaker-fidelity and text-intelligibility remains a challenge, particularly when diverse control demands are considered. Addressing this, we introduce DualSpeech, a TTS model that integrates phoneme-level latent diffusion with dual classifier-free guidance. This approach enables exceptional control over speaker-fidelity and text-intelligibility. Experimental results demonstrate that by utilizing the sophisticated control, DualSpeech surpasses existing state-of-the-art TTS models in performance. Demos are available at https://bit.ly/48Ewoib.<|reference_end|>
@article{yang2024dualspeech:,
    title={DualSpeech: Enhancing Speaker-Fidelity and Text-Intelligibility Through
  Dual Classifier-Free Guidance},
    author={Jinhyeok Yang, Junhyeok Lee, Hyeong-Seok Choi, Seunghun Ji, Hyeongju
  Kim, Juheon Lee},
    journal={arXiv preprint arXiv:2408.14423},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14423},
    primaryClass={eess.AS cs.SD}
}