Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training

Bing Han; Guanrou Yang; Hui Wang; Long Zhou; Tianrui Wang; Wei Wang; Xie Chen; Xu Tan; Yifan Yang; Zengrui Jin

arxiv: 2601.03065 · v3 · submitted 2026-01-06 · 📡 eess.AS

Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training

Yifan Yang , Bing Han , Hui Wang , Wei Wang , Ziyang Ma , Long Zhou , Zengrui Jin , Guanrou Yang

show 3 more authors

Tianrui Wang Xu Tan Xie Chen

This is my paper

Pith reviewed 2026-05-16 16:40 UTC · model grok-4.3

classification 📡 eess.AS

keywords contrastive pre-trainingfine-grained speech captionsmulti-granular representationsspeech-text alignmentparalinguistic classificationstyle similarity

0 comments

The pith

CLSP uses fine-grained speech captions to learn unified representations across multiple granularities

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces FCaps, a dataset of 47k hours of speech paired with 19M fine-grained free-text style descriptions created through an end-to-end grounding pipeline. It then trains CLSP, a contrastive language-speech model that combines global and fine-grained supervision signals. This produces representations that support both coarse and detailed speech-text tasks. Experiments show reliable results on retrieval, zero-shot paralinguistic classification, and style similarity scoring that align with human judgments.

Core claim

Building on FCaps, CLSP integrates global and fine-grained supervision to learn speech-text representations that operate reliably across multiple granularities, performing well on global and fine-grained retrieval, zero-shot paralinguistic classification, and speech style similarity scoring with strong human alignment.

What carries the argument

CLSP, the contrastive language-speech pre-trained model that combines global-level and fine-grained supervision signals on the FCaps dataset

If this is right

CLSP representations support both coarse retrieval and fine-grained style matching in a single model
Zero-shot paralinguistic classification becomes possible without task-specific fine-tuning
Speech style similarity scoring aligns closely enough with human judgments to serve as a proxy metric

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same fine-grained captioning pipeline could be applied to other audio domains such as music or environmental sound to create style-aware representations
Multi-granular contrastive training might reduce the need for separate models for global versus detailed speech understanding tasks
If the approach scales, it could improve downstream applications like expressive speech synthesis by providing richer style control signals

Load-bearing premise

The end-to-end pipeline produces accurate fine-grained captions without the errors of cascaded LLM rewriting, and LLM-as-a-judge scores reliably reflect human standards for correctness and coverage.

What would settle it

A direct comparison where CLSP's style similarity scores show low correlation with human raters on the same speech clips would undermine the claim of multi-granular alignment.

Figures

Figures reproduced from arXiv: 2601.03065 by Bing Han, Guanrou Yang, Hui Wang, Long Zhou, Tianrui Wang, Wei Wang, Xie Chen, Xu Tan, Yifan Yang, Zengrui Jin, Ziyang Ma.

**Figure 2.** Figure 2: Overview of our end-to-end annotation pipeline for generating fine-grained captions, consisting of a [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of CLSP. both speech and general audio, and achieves stateof-the-art performance across a range of speech and audio benchmarks, making it well-suited for capturing fine-grained acoustic and paralinguistic cues in speaker-centric contrastive learning. Text Encoder RoBERTa-base4 is used as the text encoder. We adopt variable-length inputs with a maximum of 512 tokens to accommodate both global capt… view at source ↗

**Figure 4.** Figure 4: Pairwise comparison between end-to-end and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Annotation UI for raters to annotate the alignment score between one audio and several candidate captions. [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗

**Figure 6.** Figure 6: Correlation analysis between model-predicted similarity scores and subjective human ratings across [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

**Figure 7.** Figure 7: User prompt for detailed captioner. Detailed Captioner User Prompt w/ Tags Your task is to generate a caption describing only the characteristics of the speaker's voice. Audio: {audio} Use the following tags in the caption: - Accent: {accent} - Speaking Rate: {speaking_rate} - Emotion / Expressiveness: {situational_tags} CRITICAL RULES 1. NEVER describe the content of the speech. Do not quote any words or … view at source ↗

**Figure 8.** Figure 8: User prompt with human-annotated tags for detailed captioner. [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗

**Figure 9.** Figure 9: Detailed protocol of LLM-as-Judges. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗

read the original abstract

Modeling fine-grained speaking styles remains challenging for language-speech representation pre-training, as existing speech-text models are typically trained with coarse captions or task-specific supervision, and scalable fine-grained style annotations are unavailable. We present FCaps, a large-scale dataset with fine-grained free-text style descriptions, encompassing 47k hours of speech and 19M fine-grained captions annotated via a novel end-to-end pipeline that directly grounds detailed captions in audio, thereby avoiding the error propagation caused by LLM-based rewriting in existing cascaded pipelines. Evaluations using LLM-as-a-judge demonstrate that our annotations surpass existing cascaded annotations in terms of correctness, coverage, and naturalness. Building on FCaps, we propose CLSP, a contrastive language-speech pre-trained model that integrates global and fine-grained supervision, enabling unified representations across multiple granularities. Extensive experiments demonstrate that CLSP learns fine-grained and multi-granular speech-text representations that perform reliably across global and fine-grained speech-text retrieval, zero-shot paralinguistic classification, and speech style similarity scoring, with strong alignment to human judgments. Code and dataset are publicly available at https://github.com/yfyeung/CLSP.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper adds a new end-to-end fine-grained speech caption dataset and a contrastive model that trains on both global and detailed alignments, but the dataset quality edge rests on unverified LLM-as-a-judge scores.

read the letter

The real contribution here is FCaps, a 47k-hour dataset with 19M detailed style captions created by grounding text directly in audio rather than running through cascaded LLM rewrites. That pipeline choice is sensible and avoids one obvious source of compounding errors. CLSP then trains a single contrastive model on both coarse and fine-grained pairs, which lets it handle retrieval at multiple levels plus zero-shot paralinguistic classification and style similarity scoring. Releasing the code and data is helpful for anyone who wants to check or extend the work. The multi-granular supervision idea fits speech well, where global semantics and local prosody both matter, and the reported results show consistent gains across those tasks. The soft spot is the validation of the captions themselves. The abstract claims the end-to-end annotations beat cascaded ones on correctness, coverage, and naturalness via LLM-as-a-judge, yet no human correlation numbers or inter-rater agreement figures are given. Without those, it is hard to know whether the claimed quality advantage is solid or partly an artifact of the judge. The model outputs are said to align with human judgments, but that does not fully substitute for checking the training labels. This is aimed at people working on speech-language pretraining who need better style capture for synthesis or understanding tasks. It has enough concrete new data and a testable method to deserve peer review, even if the reviewers will likely push for human validation numbers on the dataset step.

Referee Report

3 major / 2 minor

Summary. The paper introduces FCaps, a 47k-hour speech dataset with 19M fine-grained free-text style captions generated by a novel end-to-end pipeline that directly grounds captions in audio to avoid cascaded LLM rewriting errors. It then proposes CLSP, a contrastive language-speech pre-training model that combines global and fine-grained supervision to produce unified multi-granular representations. Experiments claim strong results on global/fine-grained retrieval, zero-shot paralinguistic classification, and speech style similarity scoring, with alignment to human judgments; code and data are released.

Significance. If the central claims hold, the work supplies a scalable resource and training recipe for fine-grained speech-text modeling that could benefit paralinguistic and stylistic tasks where coarse captions have been limiting. Public release of the dataset and code is a concrete strength that would enable follow-on research.

major comments (3)

[Dataset creation / §3] Dataset creation section: the claim that FCaps annotations surpass cascaded baselines on correctness, coverage, and naturalness rests entirely on LLM-as-a-judge scores; no human correlation coefficient, inter-rater agreement, or even a small-scale human validation study is reported. Because the contrastive supervision in CLSP is directly derived from these captions, this unquantified alignment is load-bearing for the multi-granular performance claims.
[Experiments / §4] Experimental results (Tables 2–4): performance numbers for CLSP are presented without error bars, confidence intervals, or statistical significance tests across runs. In addition, the data splits used for the fine-grained retrieval and style-similarity tasks are not fully specified, preventing independent verification of the reported gains over baselines.
[§2.2] Model description (§2.2): the precise formulation of the joint contrastive loss that integrates global and fine-grained supervision is only sketched at a high level; it is unclear whether the fine-grained term is applied at the same temperature or with the same negative sampling strategy as the global term, which directly affects whether the multi-granular unification is achieved by design or by hyper-parameter tuning.

minor comments (2)

[Abstract] Abstract: the phrase “strong alignment to human judgments” is repeated without any numeric correlation value; a single sentence reporting the actual human–LLM agreement on a held-out subset would strengthen the claim.
[§2] Notation: the terms “global,” “fine-grained,” and “multi-granular” are used interchangeably in places; a short definitions paragraph or table would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below, indicating the revisions we will incorporate to strengthen the manuscript.

read point-by-point responses

Referee: [Dataset creation / §3] Dataset creation section: the claim that FCaps annotations surpass cascaded baselines on correctness, coverage, and naturalness rests entirely on LLM-as-a-judge scores; no human correlation coefficient, inter-rater agreement, or even a small-scale human validation study is reported. Because the contrastive supervision in CLSP is directly derived from these captions, this unquantified alignment is load-bearing for the multi-granular performance claims.

Authors: We agree that the absence of human validation metrics is a limitation, as the quality of FCaps directly supports the CLSP training. In the revised manuscript, we will add a small-scale human evaluation on a random subset of captions, reporting inter-rater agreement (e.g., Fleiss' kappa) and Pearson/Spearman correlations between LLM-as-a-judge scores and human ratings for correctness, coverage, and naturalness. This will quantify the alignment and bolster the claims. revision: yes
Referee: [Experiments / §4] Experimental results (Tables 2–4): performance numbers for CLSP are presented without error bars, confidence intervals, or statistical significance tests across runs. In addition, the data splits used for the fine-grained retrieval and style-similarity tasks are not fully specified, preventing independent verification of the reported gains over baselines.

Authors: We acknowledge that reporting variability and full split details is necessary for reproducibility. We will rerun all experiments with at least three random seeds, add error bars and 95% confidence intervals to Tables 2–4, and include paired t-tests or Wilcoxon tests for significance against baselines. A new appendix will fully document the train/validation/test splits for every task, including fine-grained retrieval and style-similarity, with exact indices or generation procedures. revision: yes
Referee: [§2.2] Model description (§2.2): the precise formulation of the joint contrastive loss that integrates global and fine-grained supervision is only sketched at a high level; it is unclear whether the fine-grained term is applied at the same temperature or with the same negative sampling strategy as the global term, which directly affects whether the multi-granular unification is achieved by design or by hyper-parameter tuning.

Authors: We will expand §2.2 with the exact loss equations. The joint loss is L = L_global + λ L_fine, where both terms use the identical temperature τ and the same in-batch negative sampling (no additional negatives for the fine-grained term). This design choice is fixed in the released code; the revision will make the shared hyperparameters explicit so that unification is shown to be achieved by construction rather than tuning. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces the FCaps dataset via a new end-to-end annotation pipeline and trains the CLSP model using standard contrastive objectives on global and fine-grained speech-text pairs. All performance claims rest on empirical results from retrieval, classification, and similarity tasks rather than any closed-form derivations, fitted parameters renamed as predictions, or self-citation chains that reduce the central claims to inputs by construction. No equations, uniqueness theorems, or ansatzes are invoked that loop back to the paper's own definitions or prior self-citations in a load-bearing way; the LLM-as-a-judge step is an external evaluation tool, not a self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on standard contrastive learning assumptions plus the unproven reliability of LLM-as-a-judge for caption quality; no free parameters or invented entities are introduced in the abstract.

axioms (1)

domain assumption LLM-as-a-judge evaluations can accurately reflect human judgments on caption correctness, coverage, and naturalness.
Invoked to validate the FCaps annotations against cascaded baselines.

pith-pipeline@v0.9.0 · 5535 in / 1359 out tokens · 91513 ms · 2026-05-16T16:40:02.986403+00:00 · methodology

discussion (0)

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Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We adopt a symmetric InfoNCE loss... multi-positive InfoNCE loss... Stage One... Stage Two... Task 1... Task 2
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CLSP... dual-encoder architecture... SPEAR-XLarge... RoBERTa-base... fine-grained and multi-granular contrastive supervision

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Reference graph

Works this paper leans on

14 extracted references · 14 canonical work pages · 2 internal anchors

[1]

PromptTTS: controllable text-to-speech with text descriptions. InProc. ICASSP, Rhodes Island. Jiarui Hai, Karan Thakkar, Helin Wang, and 1 others

work page
[2]

DreamV oice: Text-guided voice conversion. InProc. Interspeech, Kos Island. Haorui He, Zengqiang Shang, Chaoren Wang, and 1 oth- ers. 2024. Emilia: An extensive, multilingual, and diverse speech dataset for large-scale speech genera- tion. InProc. SLT, Macao. Kaiming He, Haoqi Fan, Yuxin Wu, and 1 others. 2020. Momentum contrast for unsupervised visual re...

work page arXiv 2024
[3]

ControlSpeech: Towards simultaneous and independent zero-shot speaker cloning and zero-shot language style control. InProc. ACL, Vienna. Shengpeng Ji, Jialong Zuo, Minghui Fang, and 1 others

work page
[4]

Textrolspeech: A text style control speech corpus with codec language text-to-speech models. InProc. ICASSP, Seoul. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Men- sch, and 1 others. 2023. Mistral 7b.Preprint, arXiv:2310.06825. Zeyu Jin, Jia Jia, Qixin Wang, and 1 others. 2024. SpeechCraft: A fine-grained expressive speech dataset with natural langua...

work page internal anchor Pith review Pith/arXiv arXiv 2023
[5]

Omni-captioner: Data pipeline, mod- els, and benchmark for omni detailed perception,

Omni-captioner: Data pipeline, models, and benchmark for omni detailed perception.Preprint, arXiv:2510.12720. Ziyang Ma, Zhisheng Zheng, Jiaxin Ye, and 1 others

work page arXiv
[6]

emotion2vec: Self-supervised pre-training for speech emotion representation. InProc. ACL, Bangkok. Arsha Nagrani, Joon Son Chung, and Andrew Zisser- man. 2017. V oxCeleb: A large-scale speaker identifi- cation dataset. InProc. Interspeech, Stockholm. Tu Anh Nguyen, Wei-Ning Hsu, Antony D’Avirro, and 1 others. 2023. Expresso: A benchmark and analysis of di...

work page internal anchor Pith review Pith/arXiv arXiv 2017
[7]

EARS: an anechoic fullband speech dataset benchmarked for speech enhancement and derever- beration. InProc. Interspeech, Kos Island. Haoqin Sun, Jingguang Tian, Jiaming Zhou, and 1 oth- ers. 2025. RA-CLAP: relation-augmented emotional speaking style contrastive language-audio pretraining for speech retrieval. InProc. Interspeech, Rotterdam. Helin Wang, Ji...

work page 2025
[8]

of of the idea that has been the same idea for a thousand years that they believe that—

CapSpeech: enabling downstream applica- tions in style-captioned text-to-speech.Preprint, arXiv:2506.02863. Yusong Wu, Ke Chen, Tianyu Zhang, and 1 others. 2023. Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmen- tation. InProc. ICASSP, Rhodes. Guanrou Yang, Chen Yang, Qian Chen, and 1 others. 2025a. EmoV...

work page arXiv 2023
[13]

Good Example A young male with a clear, medium-high pitched voice and an American accent speaks in a casual, conversational style, much like a reviewer or vlogger

Failure to follow these rules will result in an invalid output. Good Example A young male with a clear, medium-high pitched voice and an American accent speaks in a casual, conversational style, much like a reviewer or vlogger. He begins at a fast, rushed pace with a highly energetic and emphatic intonation, using a high pitch to express strong emphasis. ...

work page
[14]

Do not quote any words or phrases

NEVER describe the content of the speech. Do not quote any words or phrases. NEVER contain quotation marks ("")

work page
[15]

NEVER describe background, environment, audio quality

FOCUS ONLY ON THE HUMAN VOICE. NEVER describe background, environment, audio quality

work page
[16]

NEVER mention the absence of characteristics (describe only what is present, not mention what is not present)

work page
[17]

NEVER over-interpret or guess

work page
[18]

explanation

Failure to follow these rules will result in an invalid output. Good Example A young male with a clear, medium-high pitched voice and an American accent speaks in a casual, conversational style, much like a reviewer or vlogger. He begins at a fast, rushed pace with a highly energetic and emphatic intonation, using a high pitch to express strong emphasis. ...

work page

[1] [1]

PromptTTS: controllable text-to-speech with text descriptions. InProc. ICASSP, Rhodes Island. Jiarui Hai, Karan Thakkar, Helin Wang, and 1 others

work page

[2] [2]

DreamV oice: Text-guided voice conversion. InProc. Interspeech, Kos Island. Haorui He, Zengqiang Shang, Chaoren Wang, and 1 oth- ers. 2024. Emilia: An extensive, multilingual, and diverse speech dataset for large-scale speech genera- tion. InProc. SLT, Macao. Kaiming He, Haoqi Fan, Yuxin Wu, and 1 others. 2020. Momentum contrast for unsupervised visual re...

work page arXiv 2024

[3] [3]

ControlSpeech: Towards simultaneous and independent zero-shot speaker cloning and zero-shot language style control. InProc. ACL, Vienna. Shengpeng Ji, Jialong Zuo, Minghui Fang, and 1 others

work page

[4] [4]

Textrolspeech: A text style control speech corpus with codec language text-to-speech models. InProc. ICASSP, Seoul. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Men- sch, and 1 others. 2023. Mistral 7b.Preprint, arXiv:2310.06825. Zeyu Jin, Jia Jia, Qixin Wang, and 1 others. 2024. SpeechCraft: A fine-grained expressive speech dataset with natural langua...

work page internal anchor Pith review Pith/arXiv arXiv 2023

[5] [5]

Omni-captioner: Data pipeline, mod- els, and benchmark for omni detailed perception,

Omni-captioner: Data pipeline, models, and benchmark for omni detailed perception.Preprint, arXiv:2510.12720. Ziyang Ma, Zhisheng Zheng, Jiaxin Ye, and 1 others

work page arXiv

[6] [6]

emotion2vec: Self-supervised pre-training for speech emotion representation. InProc. ACL, Bangkok. Arsha Nagrani, Joon Son Chung, and Andrew Zisser- man. 2017. V oxCeleb: A large-scale speaker identifi- cation dataset. InProc. Interspeech, Stockholm. Tu Anh Nguyen, Wei-Ning Hsu, Antony D’Avirro, and 1 others. 2023. Expresso: A benchmark and analysis of di...

work page internal anchor Pith review Pith/arXiv arXiv 2017

[7] [7]

EARS: an anechoic fullband speech dataset benchmarked for speech enhancement and derever- beration. InProc. Interspeech, Kos Island. Haoqin Sun, Jingguang Tian, Jiaming Zhou, and 1 oth- ers. 2025. RA-CLAP: relation-augmented emotional speaking style contrastive language-audio pretraining for speech retrieval. InProc. Interspeech, Rotterdam. Helin Wang, Ji...

work page 2025

[8] [8]

of of the idea that has been the same idea for a thousand years that they believe that—

CapSpeech: enabling downstream applica- tions in style-captioned text-to-speech.Preprint, arXiv:2506.02863. Yusong Wu, Ke Chen, Tianyu Zhang, and 1 others. 2023. Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmen- tation. InProc. ICASSP, Rhodes. Guanrou Yang, Chen Yang, Qian Chen, and 1 others. 2025a. EmoV...

work page arXiv 2023

[9] [13]

Good Example A young male with a clear, medium-high pitched voice and an American accent speaks in a casual, conversational style, much like a reviewer or vlogger

Failure to follow these rules will result in an invalid output. Good Example A young male with a clear, medium-high pitched voice and an American accent speaks in a casual, conversational style, much like a reviewer or vlogger. He begins at a fast, rushed pace with a highly energetic and emphatic intonation, using a high pitch to express strong emphasis. ...

work page

[10] [14]

Do not quote any words or phrases

NEVER describe the content of the speech. Do not quote any words or phrases. NEVER contain quotation marks ("")

work page

[11] [15]

NEVER describe background, environment, audio quality

FOCUS ONLY ON THE HUMAN VOICE. NEVER describe background, environment, audio quality

work page

[12] [16]

NEVER mention the absence of characteristics (describe only what is present, not mention what is not present)

work page

[13] [17]

NEVER over-interpret or guess

work page

[14] [18]

explanation

Failure to follow these rules will result in an invalid output. Good Example A young male with a clear, medium-high pitched voice and an American accent speaks in a casual, conversational style, much like a reviewer or vlogger. He begins at a fast, rushed pace with a highly energetic and emphatic intonation, using a high pitch to express strong emphasis. ...

work page