SegINR: Segment-wise Implicit Neural Representation for Sequence Alignment in Neural Text-to-Speech

• URL: http://arxiv.org/abs/2410.04690v1
• Date: Mon, 7 Oct 2024 02:04:58 GMT
• Title: SegINR: Segment-wise Implicit Neural Representation for Sequence Alignment in Neural Text-to-Speech
• Authors: Minchan Kim, Myeonghun Jeong, Joun Yeop Lee, Nam Soo Kim,
• Abstract summary: We present SegINR, a novel approach to neural Text-to-Speech (TTS) It addresses sequence alignment without relying on an auxiliary duration predictor and complex autoregressive (AR) or non-autoregressive (NAR) frame-level sequence modeling. Our experiments in zero-shot adaptive TTS scenarios demonstrate that SegINR outperforms conventional methods in speech quality with computational efficiency.
• Score: 13.82115484420239
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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