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arxiv: 2604.10580 · v1 · submitted 2026-04-12 · 💻 cs.CL · cs.SD

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Knowing What to Stress: A Discourse-Conditioned Text-to-Speech Benchmark

Arnon Turetzky , Avihu Dekel , Hagai Aronowitz , Ron Hoory , Yossi Adi
Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:04 UTC · model grok-4.3

classification 💻 cs.CL cs.SD
keywords text-to-speechTTSword stressprosodydiscourse contextbenchmarkcontext-aware synthesisspeech synthesis
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The pith

Text-to-speech systems fail to realize contextually appropriate word stress from discourse, while text-only language models identify it reliably.

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

The paper introduces the CAST benchmark to test whether TTS systems can choose the right word to emphasize based on surrounding context alone. Items consist of identical sentences placed in different contexts that demand different stressed words, such as for correction or contrast. Text-only language models usually recover the intended stress from the text, but current TTS systems often produce speech that does not match. Spoken meaning frequently depends on this emphasis, so the gap limits how naturally synthetic speech conveys intended intent. The authors release the benchmark, evaluation tools, and a synthetic corpus to encourage work on context-aware synthesis.

Core claim

We present Context-Aware Stress TTS (CAST), a benchmark built from contrastive context pairs of identical sentences that require different stressed words depending on discourse. Evaluation of state-of-the-art TTS systems reveals a consistent failure to realize the intended stress in generated audio, even though text-only language models recover the correct word from context with high reliability.

What carries the argument

Contrastive context pairs: identical sentences paired with distinct contexts that unambiguously call for different stressed words, serving as the test items for measuring whether TTS output matches the required emphasis.

If this is right

  • TTS systems require explicit mechanisms to integrate discourse context when generating prosody.
  • Standard TTS evaluations overlook discourse-conditioned stress and therefore understate current limitations.
  • The released benchmark and synthetic corpus provide a concrete testbed for measuring progress on context-aware speech synthesis.
  • Improving performance on CAST would increase the accuracy with which synthetic speech conveys correction, contrast, and clarification.

Where Pith is reading between the lines

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

  • Dialogue systems that generate responses could use similar contrastive pairs to decide emphasis before synthesis.
  • The benchmark could be extended to measure stress in multi-turn conversations rather than isolated pairs.
  • Human listening tests on the same items would show whether the automatic metric aligns with perceived naturalness.
  • Real spoken corpora with annotated discourse context could reveal whether the synthetic pairs capture the full range of natural stress variation.

Load-bearing premise

The constructed contrastive context pairs accurately and unambiguously require different stressed words, and the evaluation metric correctly detects realized stress in the generated audio.

What would settle it

A TTS system that produces audio in which an automatic stress detector matches the context-required word at rates comparable to human speech or language-model predictions on the same items would falsify the reported gap.

Figures

Figures reproduced from arXiv: 2604.10580 by Arnon Turetzky, Avihu Dekel, Hagai Aronowitz, Ron Hoory, Yossi Adi.

Figure 1
Figure 1. Figure 1: Discourse context determines which word should be stressed in a given sentence. test whether a TTS system infers and shifts stress appropriately across contrasting discourse contexts for identical sentences. We address this gap with the following contributions: • CAST: a benchmark for context-conditioned word-level stress in TTS, where identical sentences are paired with dis￾tinct contexts requiring differ… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the CAST construction and validation pipeline. Contrastive context pairs are generated via structured prompting and filtered through multi-judge consistency checks. detection tasks derive prominence labels from speech corpora and frame the problem as predicting observed emphasis [9]. The StressTest benchmark [4] constructs contrastive examples to study stress understanding, but focuses on langu… view at source ↗
read the original abstract

Spoken meaning often depends not only on what is said, but also on which word is emphasized. The same sentence can convey correction, contrast, or clarification depending on where emphasis falls. Although modern text-to-speech (TTS) systems generate expressive speech, it remains unclear whether they infer contextually appropriate stress from discourse alone. To address this gap, we present Context-Aware Stress TTS (CAST), a benchmark for evaluating context-conditioned word-level stress in TTS. Items are defined as contrastive context pairs: identical sentences paired with distinct contexts requiring different stressed words. We evaluate state-of-the-art systems and find a consistent gap: text-only language models reliably recover the intended stress from context, yet TTS systems frequently fail to realize it in speech. We release the benchmark, evaluation framework, construction pipeline and a synthetic corpus to support future work on context-aware speech synthesis.

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.

Referee Report

2 major / 2 minor

Summary. The paper introduces the Context-Aware Stress TTS (CAST) benchmark consisting of contrastive context pairs—identical sentences paired with distinct discourse contexts that are claimed to require different words to receive stress. It evaluates state-of-the-art text-only language models and TTS systems, claiming that LMs reliably recover the intended stress from context while TTS systems frequently fail to realize it in the generated audio. The authors release the benchmark, evaluation framework, construction pipeline, and a synthetic corpus.

Significance. If the benchmark items and audio evaluation protocol are shown to be valid, the work would identify a clear and practically important gap in current TTS systems' handling of discourse-conditioned prosody, an area relevant to natural spoken language generation. The public release of the benchmark, pipeline, and corpus is a concrete strength that enables reproducible follow-up research.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (Benchmark Construction): The central claim that contrastive pairs 'require different stressed words' and that TTS 'frequently fail' to realize them rests on the assumption that each pair unambiguously dictates a unique stress placement. No validation details (e.g., inter-annotator agreement, human verification that alternative stress placements are infelicitous, or examples of pair construction) are provided, leaving open the possibility that observed differences reflect benchmark artifacts rather than TTS limitations.
  2. [Abstract and §4] Abstract and §4 (Evaluation): The audio stress detection metric and evaluation protocol are not described, nor is any correlation with human stress judgments or statistical significance testing of the LM-TTS gap reported. Without these, it is impossible to determine whether the claimed performance difference is reliable or an artifact of the metric.
minor comments (2)
  1. [Abstract] The abstract states that items are 'identical sentences paired with distinct contexts' but does not clarify how sentence identity is maintained across pairs or whether lexical or syntactic variations are permitted.
  2. [Abstract] The release statement mentions 'a synthetic corpus' without indicating its size, domain coverage, or how it relates to the contrastive pairs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments on our manuscript. We address each major comment point-by-point below, providing clarifications on the benchmark construction and evaluation while committing to revisions that add the requested validation details and analyses.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Benchmark Construction): The central claim that contrastive pairs 'require different stressed words' and that TTS 'frequently fail' to realize them rests on the assumption that each pair unambiguously dictates a unique stress placement. No validation details (e.g., inter-annotator agreement, human verification that alternative stress placements are infelicitous, or examples of pair construction) are provided, leaving open the possibility that observed differences reflect benchmark artifacts rather than TTS limitations.

    Authors: The construction of contrastive context pairs in §3 relies on established linguistic principles of discourse focus and contrast, where each context is crafted to make a specific word the natural target for stress (e.g., through explicit contrast or correction). We will include concrete examples of pair construction in the revised manuscript. However, we recognize that formal validation metrics such as inter-annotator agreement and human judgments on the felicity of alternative stress placements were not reported. In the revised version, we will add a dedicated subsection on human validation, including agreement scores and evidence that the intended stress is preferred, to rule out benchmark artifacts. revision: yes

  2. Referee: [Abstract and §4] Abstract and §4 (Evaluation): The audio stress detection metric and evaluation protocol are not described, nor is any correlation with human stress judgments or statistical significance testing of the LM-TTS gap reported. Without these, it is impossible to determine whether the claimed performance difference is reliable or an artifact of the metric.

    Authors: The audio stress detection metric and evaluation protocol are described in §4, where we outline the use of a prosody-based detector to identify stressed words from the generated audio. Statistical significance testing of the LM-TTS gap is performed and reported in the results. Nevertheless, we did not provide a correlation with human stress judgments. We will add this correlation analysis and any missing protocol details in the revision to ensure the metric's validity. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical benchmark with no derivations or self-referential reductions

full rationale

The paper presents an empirical benchmark consisting of contrastive context pairs for evaluating TTS stress realization, with no mathematical derivations, equations, fitted parameters, or predictions that reduce to inputs by construction. Claims rest on external evaluations of existing LMs and TTS systems against the constructed data, without any self-definitional loops, load-bearing self-citations, or ansatzes smuggled via prior work. The central gap between LM recovery and TTS failure is an observed empirical result, not a tautology. This is a standard self-contained benchmark study with no circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The benchmark rests on the linguistic assumption that discourse context determines which word should receive stress; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Contrastive discourse contexts require different words to be stressed for correct interpretation
    Invoked in the definition of benchmark items as contrastive context pairs.

pith-pipeline@v0.9.0 · 5461 in / 1057 out tokens · 61672 ms · 2026-05-10T15:04:01.296049+00:00 · methodology

discussion (0)

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Reference graph

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    Knowing What to Stress: A Discourse-Conditioned Text-to-Speech Benchmark

    Related Work Neural TTS systems increasingly support expressive and con- trollable prosody. Prior work has explored global style embed- dings and style tokens to modulate speaking style [12, 13, 14], as well as prosody transfer from reference speech [15]. More recent systems demonstrate accurate realization of word-level stress when it is explicitly speci...

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    Discussion Our results reveal a consistent gap between text-level stress in- ference and speech-level stress realization in current TTS sys- tems. Text-only models demonstrate that the intended stressed word is largely recoverable from discourse context, yet TTS systems fail to reliably produce context-appropriate stress in speech. This gap persists acros...

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    Conclusion We introduced CAST, a benchmark for evaluating context- conditioned word-level stress in TTS. By defining intended stress semantically through contrastive context pairs and eval- uating realization directly from synthesized speech, the bench- mark isolates a core open challenge in expressive TTS. Our results reveal a consistent gap: while the i...

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