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Language-Statistical Analysis of Neural Audio Codec Tokens Across Architectures, Corpora, and Noise Conditions

Paper recorded by Signals 4 on 2026-08-31 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.CL · 自然语言处理 · first seen 2026-09-01

Abstract

Neural audio codecs (NACs) convert speech into discrete token sequences, and prior work has reported that these sequences follow language-like statistical laws. This paper analyzes the token statistics of 13 NACs spanning multi-codebook residual vector quantization (RVQ), single-codebook VQ, and non-VQ designs, evaluated on three corpora under clean, white-noise, and real-world DEMAND-noise condit

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#159 most recent of 186 cs.CL papers we have recorded · ↑ newer: Type-Balanced Contextual Learning for Incremental Named Entity Recogni · ↓ older: When Does Predictor-Based RL Align with Human Perception? A Study of S
Cite this page: Language-Statistical Analysis of Neural Audio Codec Tokens Across Architectures, Corpora, and Noise Conditions: the #159 most recent of 186 cs.CL papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/language-statistical-analysis-of-neural-audio-codec-tokens-across-architectures-.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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