---
title: "TiCodec：通过时间不变表示实现流式神经语音编解码"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.05250v1"
permalink: "https://feed.astralor.com/items/itm_ad9ad5a7cc3bb7a6e92a6793"
published: "2026-07-06T15:57:49.000Z"
score: 77
channel: "tech"
tags: ["Dual-TIRE","TiCodec","TIRE","流式语音生成","神经语音编解码"]
---

# TiCodec：通过时间不变表示实现流式神经语音编解码

## AI 点评

> 这篇论文的价值在于不仅改进了编解码器的效率，还通过探测实验揭示了不同编码器层的功能分工，为未来更高效的语音表示设计提供了理论依据。对从事语音助手、实时翻译和低功耗边缘设备的工程师来说，TiCodec 的架构具有很强的借鉴意义。

## 速览

TiCodec 提出一种因子化表示方法，通过时间不变表示提取（TIRE）模块将语音内容与时间不变信息分离，减少帧级建模的信息量。研究发现中间编码器层捕获说话人与环境信息而几乎不含语言内容，跨文件采样提升鲁棒性。在此基础上提出 Dual-TIRE 多层级架构，增强重建质量与说话人相似性。流式推理实验表明，660ms 处理块下重建性能几乎无损失，为低延迟语音生成系统开辟新路径。

## 关键点

- 因子化表示将时变语音内容与时不变信息分离，降低帧级建模复杂度
- 中间编码器层主要捕获说话人与环境特征，几乎不含语言信息
- 跨文件采样策略显著提升时间不变表示的鲁棒性
- Dual-TIRE 多层级架构利用各层互补性改善重建质量与说话人相似度

## 全文

Neural speech codecs are increasingly used as intermediate representations in codec-based speech generation systems. TiCodec introduces a factorized representation that separates time-varying speech content from time-invariant information through a Time-Invariant Representation Extraction (TIRE) module, potentially reducing the amount of information that must be modeled at the frame-level. In this work, we investigate the nature of the information captured by TIRE representations and their suitability for low-latency speech processing. Using a series of probing tasks, we analyze the influence of the encoder layer and show that intermediate layers capture complementary speaker- and environment-related information while containing little linguistic content. We further study several segment selection strategies for TIRE training and demonstrate that cross-file sampling improves the robustness of invariant representations. Based on these findings, we propose Dual-TIRE, a multi-level architecture that exploits the complementarity of different encoder layers and improves speech reconstruction quality and speaker similarity. Finally, we evaluate TiCodec in a streaming inference setting using successive 660ms processing blocks. Results show that streaming operation can be achieved without significant degradation in reconstruction performance, highlighting the potential of factorized neural codec representations for future low-latency speech generation systems.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_ad9ad5a7cc3bb7a6e92a6793