SegINR: Segment-wise Implicit Neural Representation for Sequence Alignment in Neural Text-to-Speech
<|reference_start|>SegINR: Segment-wise Implicit Neural Representation for Sequence Alignment in Neural Text-to-Speech: We present SegINR, a novel approach to neural Text-to-Speech (TTS) that addresses sequence alignment without relying on an auxiliary duration predictor and complex autoregressive (AR) or non-autoregressive (NAR) frame-level sequence modeling. SegINR simplifies the process by converting text sequences directly into frame-level features. It leverages an optimal text encoder to extract embeddings, transforming each into a segment of frame-level features using a conditional implicit neural representation (INR). This method, named segment-wise INR (SegINR), models temporal dynamics within each segment and autonomously defines segment boundaries, reducing computational costs. We integrate SegINR into a two-stage TTS framework, using it for semantic token prediction. Our experiments in zero-shot adaptive TTS scenarios demonstrate that SegINR outperforms conventional methods in speech quality with computational efficiency.<|reference_end|>
@article{kim2024seginr:,
    title={SegINR: Segment-wise Implicit Neural Representation for Sequence
  Alignment in Neural Text-to-Speech},
    author={Minchan Kim, Myeonghun Jeong, Joun Yeop Lee, Nam Soo Kim},
    journal={arXiv preprint arXiv:2410.04690},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04690},
    primaryClass={eess.AS cs.LG}
}