S2S-Arena: Evaluating Paralinguistic Instruction Following in Speech-to-Speech Models

Benyou Wang; Fan Bu; Feng Jiang; Haizhou Li; Liumeng Xue; Xiangying Chen; Yiyang Liu; Yuhao Du; Zhiyu Lin

arxiv: 2503.05085 · v2 · submitted 2025-03-07 · 💻 cs.CL · cs.SD· eess.AS

S2S-Arena: Evaluating Paralinguistic Instruction Following in Speech-to-Speech Models

Feng Jiang , Zhiyu Lin , Yiyang Liu , Liumeng Xue , Fan Bu , Yuhao Du , Xiangying Chen , Benyou Wang

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Haizhou Li

This is my paper

Pith reviewed 2026-05-23 00:30 UTC · model grok-4.3

classification 💻 cs.CL cs.SDeess.AS

keywords speech-to-speech modelsparalinguistic cuesinstruction followingbenchmark evaluationprosody and emotionpairwise comparisonreal-world tasks

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The pith

S2S-Arena benchmark reveals substantial performance gaps in paralinguistic instruction following between academic and industrial speech-to-speech systems.

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

The paper introduces S2S-Arena as a speech-native evaluation framework to address the limitation that existing benchmarks rely on text and overlook paralinguistic elements like prosody and emotion. It establishes a four-level interaction protocol that increases in paralinguistic complexity and a two-stage pipeline that generates 1,243 speech samples across more than 100 real-world tasks. An arena-style setup then performs over 1,000 pairwise comparisons directly in the speech modality on 10 state-of-the-art systems. The results show clear differences, with industrial systems handling complex demands more effectively than academic ones, and the work identifies design factors that affect expressive instruction following.

Core claim

S2S-Arena is a speech-native benchmark that uses a four-level interaction protocol, a two-stage data construction pipeline yielding 1,243 samples over 100+ tasks, and reference-free pairwise comparison to demonstrate that current S2S systems exhibit substantial performance gaps under increasing paralinguistic demands, with industrial models generally outperforming academic ones.

What carries the argument

The four-level interaction protocol combined with the two-stage data construction pipeline that generates speech samples probing paralinguistic instruction following at increasing levels of complexity.

If this is right

Industrial S2S systems maintain higher performance than academic systems when paralinguistic demands become complex.
Specific design choices in model architecture and training data control how well a system follows instructions involving prosody and emotion.
Reference-free pairwise comparison in the speech domain can surface differences that text-only metrics miss.
The identified design factors supply concrete directions for improving naturalness and robustness in future speech agents.

Where Pith is reading between the lines

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

Extending the benchmark to additional languages or longer multi-turn dialogues could test whether the observed gaps persist outside English and short exchanges.
The arena format might be adapted to compare open-source versus closed-source models on privacy-sensitive paralinguistic tasks where reference audio is unavailable.
If the performance gaps trace mainly to training data diversity, targeted data augmentation focused on emotion and speaker variation could narrow them without changing model scale.

Load-bearing premise

The four-level interaction protocol and two-stage data construction pipeline produce speech samples that accurately and unbiasedly probe paralinguistic instruction following in real-world tasks.

What would settle it

A follow-up study that collects new human preference judgments on the same model outputs using unscripted live conversations and finds either no performance gap or reversed model rankings would falsify the benchmark's core validity.

read the original abstract

Recent advances in large language models (LLMs) have fundamentally reshaped speech-to-speech (S2S) systems, enabling increasingly natural spoken interaction. However, existing benchmarks still rely heavily on text-based evaluation and largely ignore paralinguistic cues such as prosody, emotion, and speaker traits, which are central to expressive and human-like communication. We introduce S2S-Arena, a speech-native benchmark for evaluating instruction-following S2S models with explicit assessment of both semantic understanding and paralinguistic expression. S2S-Arena features a four-level interaction protocol that systematically probes models under increasing paralinguistic complexity, a two-stage data construction pipeline that produces 1,243 speech samples spanning 100+ real-world tasks, and an arena-style evaluation framework that enables reference-free, pairwise comparison directly in the speech modality. Benchmarking 10 state-of-the-art S2S systems over 1,000+ comparisons reveals substantial performance gaps (especially under complex paralinguistic demands) between current academic and industrial systems. Our analysis further identifies key design factors governing expressive instruction following, providing actionable insights for building more natural, robust, and human-aligned speech agents.

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

S2S-Arena gives a speech-native benchmark for paralinguistic instruction following with a four-level protocol and arena comparisons, but the abstract supplies almost no validation evidence for the data or judgments.

read the letter

The main point is that this paper builds a benchmark to test speech-to-speech models on following instructions that involve prosody, emotion, and speaker traits rather than just the words. It uses a four-level protocol that adds paralinguistic demands step by step, a two-stage pipeline to create 1,243 speech samples from over 100 real tasks, and pairwise arena judgments done directly on audio without text references. They ran ten systems through more than 1,000 comparisons and report clear gaps, especially on the harder cases, with industrial models ahead of academic ones. The analysis also flags some design factors that seem to matter for expressive following. That setup is new in the speech modality and directly targets the gap the abstract notes in prior text-only work. The paper does a reasonable job laying out the protocol and the scale of the evaluation. The soft spot is the missing validation. The abstract describes the construction and the results but gives no inter-annotator agreement numbers, no error analysis on the samples, and no checks on whether the two-stage pipeline produces unbiased probes. Without those, the performance-gap claim rests on accepting the protocol and data at face value. The stress-test note is right that nothing in the abstract contradicts itself, but that does not substitute for the missing evidence. No equations or fitted parameters here, just empirical construction. This is for people working on speech AI and voice agents who need evaluation tools that go beyond semantics. A reader in that area would get value from the protocol description and the reported gaps even if they plan to adapt or re-validate the benchmark themselves. It deserves a serious referee to examine the methods section and any validation that is in the full paper.

Referee Report

2 major / 2 minor

Summary. The paper introduces S2S-Arena, a speech-native benchmark for evaluating paralinguistic instruction following in S2S models. It proposes a four-level interaction protocol that increases paralinguistic complexity, a two-stage pipeline generating 1,243 speech samples over 100+ real-world tasks, and an arena-style reference-free pairwise evaluation in the speech modality. Benchmarking 10 S2S systems across 1,000+ comparisons shows substantial performance gaps, particularly under complex paralinguistic demands, between academic and industrial systems, along with analysis of key design factors.

Significance. If the benchmark construction and evaluation protocol are shown to be valid and unbiased, the work would provide a valuable speech-native resource that shifts evaluation away from text proxies and highlights concrete gaps in current S2S systems' handling of prosody, emotion, and speaker traits. The identification of design factors could offer actionable guidance for improving expressive instruction following.

major comments (2)

[Abstract / Benchmark Construction] The abstract and benchmark description provide no details on validation of the four-level protocol or two-stage pipeline (e.g., inter-annotator agreement for speech sample quality, error analysis of the 1,243 samples, or controls for annotator bias in paralinguistic judgments). This is load-bearing for the central performance-gap claim, as the gaps are only interpretable if the probes are shown to be accurate and unbiased.
[Evaluation Framework] The arena-style evaluation framework is described as reference-free and pairwise in the speech modality, but the manuscript supplies no quantitative evidence (e.g., agreement metrics, consistency across raters, or comparison to text-based baselines) that this protocol reliably distinguishes semantic vs. paralinguistic failures. Without such evidence the reported gaps between academic and industrial systems cannot be confidently attributed to paralinguistic demands.

minor comments (2)

[Benchmark Construction] Clarify the exact criteria used to select the 10 S2S systems and the 100+ tasks to allow reproducibility.
[Analysis] The claim of 'actionable insights' from the design-factor analysis would benefit from explicit mapping to model components (e.g., which architectural choices correlate with which paralinguistic levels).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for explicit validation of the benchmark construction and evaluation protocol. We agree these elements are important for interpreting the performance gaps and will revise the manuscript to address both major comments.

read point-by-point responses

Referee: [Abstract / Benchmark Construction] The abstract and benchmark description provide no details on validation of the four-level protocol or two-stage pipeline (e.g., inter-annotator agreement for speech sample quality, error analysis of the 1,243 samples, or controls for annotator bias in paralinguistic judgments). This is load-bearing for the central performance-gap claim, as the gaps are only interpretable if the probes are shown to be accurate and unbiased.

Authors: We agree that the current manuscript lacks these validation details. In the revision we will add a new subsection on benchmark validation that reports inter-annotator agreement for the two-stage pipeline, an error analysis of the 1,243 samples, and explicit controls for annotator bias in paralinguistic judgments. These additions will directly support the reliability of the reported gaps between academic and industrial systems. revision: yes
Referee: [Evaluation Framework] The arena-style evaluation framework is described as reference-free and pairwise in the speech modality, but the manuscript supplies no quantitative evidence (e.g., agreement metrics, consistency across raters, or comparison to text-based baselines) that this protocol reliably distinguishes semantic vs. paralinguistic failures. Without such evidence the reported gaps between academic and industrial systems cannot be confidently attributed to paralinguistic demands.

Authors: We acknowledge that the manuscript currently provides no quantitative evidence for the reliability of the arena-style protocol. In the revised version we will include rater agreement metrics, consistency statistics across raters, and a direct comparison against text-based evaluation baselines to demonstrate that the speech-native pairwise judgments reliably separate semantic from paralinguistic failures. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is an empirical benchmark paper introducing S2S-Arena with a four-level protocol, two-stage data pipeline, and arena-style evaluation. The abstract and description contain no equations, fitted parameters, predictions, or derivations that could reduce to inputs by construction. No self-citation load-bearing steps, uniqueness theorems, or ansatzes are present. The performance-gap claim follows directly from applying the described construction and comparison methods to the 10 systems; the work is self-contained as a benchmark without internal circular reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical derivations or new physical entities; the contribution rests on the benchmark design choices themselves.

pith-pipeline@v0.9.0 · 5776 in / 1068 out tokens · 80533 ms · 2026-05-23T00:30:23.743204+00:00 · methodology

discussion (0)

Forward citations

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