DPI-TTS: Directional Patch Interaction for Fast-Converging and Style Temporal Modeling in Text-to-Speech
<|reference_start|>DPI-TTS: Directional Patch Interaction for Fast-Converging and Style Temporal Modeling in Text-to-Speech: In recent years, speech diffusion models have advanced rapidly. Alongside the widely used U-Net architecture, transformer-based models such as the Diffusion Transformer (DiT) have also gained attention. However, current DiT speech models treat Mel spectrograms as general images, which overlooks the specific acoustic properties of speech. To address these limitations, we propose a method called Directional Patch Interaction for Text-to-Speech (DPI-TTS), which builds on DiT and achieves fast training without compromising accuracy. Notably, DPI-TTS employs a low-to-high frequency, frame-by-frame progressive inference approach that aligns more closely with acoustic properties, enhancing the naturalness of the generated speech. Additionally, we introduce a fine-grained style temporal modeling method that further improves speaker style similarity. Experimental results demonstrate that our method increases the training speed by nearly 2 times and significantly outperforms the baseline models.<|reference_end|>
@article{qi2024dpi-tts:,
    title={DPI-TTS: Directional Patch Interaction for Fast-Converging and Style
  Temporal Modeling in Text-to-Speech},
    author={Xin Qi, Ruibo Fu, Zhengqi Wen, Tao Wang, Chunyu Qiang, Jianhua Tao,
  Chenxing Li, Yi Lu, Shuchen Shi, Zhiyong Wang, Xiaopeng Wang, Yuankun Xie,
  Yukun Liu, Xuefei Liu, Guanjun Li},
    journal={arXiv preprint arXiv:2409.11835},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11835},
    primaryClass={cs.SD cs.AI eess.AS}
}