Pith. sign in

REVIEW 2 cited by

SoCodec: A Semantic-Ordered Multi-Stream Speech Codec for Efficient Language Model Based Text-to-Speech Synthesis

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2409.00933 v1 pith:W7I6JLO4 submitted 2024-09-02 cs.SD eess.AS

Haohan Guo , Fenglong Xie , Kun Xie , Dongchao Yang , Dake Guo , Xixin Wu , Helen Meng This is my paper · ORCID
classification cs.SDeess.AS
keywords speechmulti-streamorderedproposedsequencecodecefficientlanguage
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

The long speech sequence has been troubling language models (LM) based TTS approaches in terms of modeling complexity and efficiency. This work proposes SoCodec, a semantic-ordered multi-stream speech codec, to address this issue. It compresses speech into a shorter, multi-stream discrete semantic sequence with multiple tokens at each frame. Meanwhile, the ordered product quantization is proposed to constrain this sequence into an ordered representation. It can be applied with a multi-stream delayed LM to achieve better autoregressive generation along both time and stream axes in TTS. The experimental result strongly demonstrates the effectiveness of the proposed approach, achieving superior performance over baseline systems even if compressing the frameshift of speech from 20ms to 240ms (12x). The ablation studies further validate the importance of learning the proposed ordered multi-stream semantic representation in pursuing shorter speech sequences for efficient LM-based TTS.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Unifying Perspective on Audio Generative Modeling: Latent Representations and Modeling Strategies

    eess.AS 2026-08 conditional novelty 6.0 of 10

    The deciding factors for modeling an audio latent are dependency horizon and conditional ambiguity, viewed jointly with the representation, not whether the latent is discrete or continuous.

  2. WavChat: A Survey of Spoken Dialogue Models

    eess.AS 2024-11 conditional novelty 4.0 of 10

    WavChat categorizes spoken dialogue models into cascaded and end-to-end paradigms and surveys speech representations, training strategies, streaming, duplex interaction, datasets, and evaluation benchmarks.

Pith tools