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arxiv: 2510.14664 · v2 · submitted 2025-10-16 · 💻 cs.SD · eess.AS

SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation

Hui Wang , Jinghua Zhao , Yifan Yang , Shujie Liu , Junyang Chen , Yanzhe Zhang , Shiwan Zhao , Jinyu Li
show 4 more authors
Jiaming Zhou Haoqin Sun Yan Lu Yong Qin
This is my paper

Pith reviewed 2026-05-18 06:21 UTC · model grok-4.3

classification 💻 cs.SD eess.AS
keywords speech quality evaluationlarge language modelsspeech assessmentdeepfake detectionmultilingual evaluationchain-of-thought reasoninginterpretability
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The pith

Large language models can evaluate synthetic speech quality with structured explanations across tasks and languages when trained on a dedicated dataset.

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

The paper establishes a new paradigm for speech quality evaluation by having large language models act as judges that provide reasoned assessments rather than single scores. It supports this with the SpeechEval dataset of 32,207 multilingual clips and 128,754 annotations spanning quality assessment, pairwise comparison, improvement suggestions, and deepfake detection. The authors then train SQ-LLM using chain-of-thought reasoning and reward optimization. Experiments demonstrate strong results across the tasks and languages, indicating that this approach can make evaluation more general and interpretable than prior scalar-based methods.

Core claim

SQ-LLM is a speech-quality-aware large language model trained with chain-of-thought reasoning and reward optimization on the SpeechEval dataset; it performs structured evaluation with explanations on quality assessment, pairwise comparison, improvement suggestion, and deepfake detection, achieving strong performance across multiple tasks and languages.

What carries the argument

SQ-LLM, the speech-quality-aware LLM that uses chain-of-thought reasoning and reward optimization to produce explanation-based judgments from the SpeechEval training data.

If this is right

  • Speech quality evaluation can shift from scalar scores to natural language explanations that include improvement suggestions.
  • The same model handles quality assessment, pairwise comparison, improvement suggestion, and deepfake detection without separate systems.
  • Evaluation generalizes across multiple languages using one trained model.
  • Generative speech systems receive more actionable feedback during development.

Where Pith is reading between the lines

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

  • This evaluation style could close the loop in speech synthesis pipelines by feeding explanations back into model training.
  • Similar LLM-judge methods might apply to assessing music generation or other audio content beyond speech.
  • Performance on entirely new speech synthesis techniques outside the current dataset remains an open test of generality.

Load-bearing premise

The human annotations collected for the SpeechEval dataset reliably capture perceptual quality judgments across the four tasks and multiple languages.

What would settle it

New human ratings on the same speech clips that consistently diverge from SQ-LLM outputs on a fresh set of languages or generation methods would show the performance does not generalize as claimed.

Figures

Figures reproduced from arXiv: 2510.14664 by Haoqin Sun, Hui Wang, Jiaming Zhou, Jinghua Zhao, Jinyu Li, Junyang Chen, Shiwan Zhao, Shujie Liu, Yan Lu, Yanzhe Zhang, Yifan Yang, Yong Qin.

Figure 1
Figure 1. Figure 1: Example interactions showcasing the core capabilities of SpeechLLM-as-Judges. The model supports: [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Per-task statistics across four languages, high [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the SpeechEval data construction process, including data collection (left), task-specific [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overview of SQ-LLM training. Stage I uses [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance of SQ-LLM across tasks and languages. Left y-axis reports LLM Scores; right y-axis shows accuracy for Deepfake Detection. systems provide solid, well-calibrated baselines with balanced EER/minDCF and stable accuracy. In contrast, untuned multimodal LLMs underper￾form and do not yield calibrated metrics, indi￾cating weak out-of-the-box reliability. Custom￾constructed model narrows the gap but is… view at source ↗
Figure 7
Figure 7. Figure 7: Categorical metadata statistics in the SpeechEval assessment data: (a) Emotion distribution, with non-neutral emotions detailed on the right; (b) Gen￾der distribution; (c) Distortion type distribution. This is due to the annotation scale for Speech Rate ranging from 1 (too slow) to 5 (too fast), where deviations from the optimal speed tend to be penal￾ized more heavily. The score distribution indicates tha… view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of assessment scores across the [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Assessment example for a low-quality speech sample. [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Assessment example for a high-quality speech sample. [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison example with a large quality gap between samples. [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison example with a small quality gap between samples. [PITH_FULL_IMAGE:figures/full_fig_p015_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Suggestion example for improving a low-quality speech sample. [PITH_FULL_IMAGE:figures/full_fig_p016_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Suggestion example for refining a high-quality speech sample. [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Speech Quality Assessment Prompt. ial extraneous content or overlooks a minor stated point (e.g., underemphasizes dynamic range being stable). 6–8 Generally focused with brief, still￾useful tangents; constraints are respected with only trivial deviations. 8–10 Stays tightly on clarity/in￾telligibility, pacing, distortion with timestamps, dynamic range stability, tone/timbre, expressive￾ness/emotion; no ir… view at source ↗
Figure 15
Figure 15. Figure 15: Speech Quality Comparison Prompt. choose appropriate, safe remedies, and avoid tech￾nical/myth errors. [Level of Detail] Assessment 0–2 Little to no actionable de￾tail; mostly restates the prompt or uses vague de￾scriptors. 2–4 High-level outline; major gaps. 4–6 Main points present but lacks important parameters; some ambiguity remains. 6–8 Strong detail with minor omissions (eg. missing one timestamp or… view at source ↗
Figure 16
Figure 16. Figure 16: Speech Quality Improvement Prompt. Deepfake Speech Detection Prompt Please determine whether the following speech sample is real or synthetic: <audio> [Decision Guidelines Start] - Real: the speech is naturally produced by a human. - Fake: the speech is generated or synthesized by a machine. [Decision Guidelines End] [Output Requirements] - Output only one word: "real" or "fake". [Output Requirements End]… view at source ↗
Figure 17
Figure 17. Figure 17: Deepfake Speech Detection Prompt. across sample sources, an imbalance in training data arises. To mitigate this, we adopt four dif￾ferent partitioning ratios (2:2:6, 4:2:4, 6:2:2, and 1:1:8) for certain sample sources to ensure suffi￾cient training samples while maintaining balanced distributions across the subsets. Finally, we verify that the resulting partitions are mutually exclusive across tasks, ensu… view at source ↗
Figure 18
Figure 18. Figure 18: Speech Quality Assessment API Prompt. Task: Evaluate the following synthesized speech based on the eight dimensions below. For each dimension, provide a score from 1 to 5 (1 = worst, 5 = best), and classify "Speech Rate" as one of the following: slow, slightly slow, suitable, slightly fast, fast. Output Format: - Overall Quality: [score] - Intelligibility: [score] Distortion: [score] - Speech Rate: [class… view at source ↗
Figure 19
Figure 19. Figure 19: Speech Quality Assessment Score API Prompt. eight fine-grained dimensions, producing compara￾ble numeric scores and textual explanations. The SQC prompt used to elicit these comparative judg￾ments is shown in [PITH_FULL_IMAGE:figures/full_fig_p023_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Speech Quality Comparison API Prompt. et al., 2025), and MiDashengLM-7B (Dinkel et al., 2025). Qwen2-Audio-7B-Instruct is a large-scale audio￾language model that accepts various audio inputs and is optimized for instruction-following in both voice chat and audio analysis modes. Its audio encoder is based on the Whisper-large-v3 model. Qwen2.5-Omni-7B is an end-to-end omni￾multimodal model designed to perc… view at source ↗
Figure 21
Figure 21. Figure 21: Pearson correlation coefficients of SQ-LLM predictions with human ratings across dimensions. The [PITH_FULL_IMAGE:figures/full_fig_p026_21.png] view at source ↗
read the original abstract

Generative speech technologies are progressing rapidly, but evaluating the perceptual quality of synthetic speech remains a core challenge. Existing methods typically rely on scalar scores or binary decisions, which lack interpretability and generalization across tasks and languages. We present SpeechLLM-as-Judges, a new paradigm for enabling large language models (LLMs) to conduct structured and explanation-based speech quality evaluation. To support this direction, we introduce SpeechEval, a large-scale dataset containing 32,207 multilingual speech clips and 128,754 annotations spanning four tasks: quality assessment, pairwise comparison, improvement suggestion, and deepfake detection. Based on this resource, we develop SQ-LLM, a speech-quality-aware LLM trained with chain-of-thought reasoning and reward optimization to improve capability. Experimental results show that SQ-LLM delivers strong performance across tasks and languages, revealing the potential of this paradigm for advancing speech quality evaluation. The relevant code, models, and data are publicly available at https://github.com/NKU-HLT/SpeechLLM-as-Judges.

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

3 major / 1 minor

Summary. The manuscript proposes the SpeechLLM-as-Judges paradigm for structured, explanation-based speech quality evaluation using LLMs. It introduces the SpeechEval dataset containing 32,207 multilingual speech clips and 128,754 annotations across four tasks (quality assessment, pairwise comparison, improvement suggestion, and deepfake detection), and develops SQ-LLM by adapting an LLM with chain-of-thought reasoning and reward optimization. The central claim is that SQ-LLM achieves strong performance across tasks and languages.

Significance. If the human annotations prove reliable, this work could advance interpretable alternatives to scalar metrics such as MOS or PESQ, especially for multilingual and multi-task speech evaluation. The public release of code, models, and data supports reproducibility and is a clear strength.

major comments (3)
  1. [§3] §3 (Dataset): No inter-annotator agreement statistics (e.g., Krippendorff’s alpha or pairwise agreement rates) are reported for the 128,754 annotations. This is load-bearing because the reliability of these labels directly determines the validity of both the reward optimization and the performance claims for SQ-LLM.
  2. [§5] §5 (Experiments): The results assert strong performance across tasks and languages but supply no quantitative metrics, baseline comparisons (e.g., against PESQ, MOS predictors, or prior LLM judges), statistical tests, or ablation studies isolating the contribution of chain-of-thought and reward optimization.
  3. [Methods] Methods: No details are given on annotation guidelines, rater screening procedures, or any post-hoc correlation of SpeechEval labels with established objective metrics (PESQ, STOI, or human MOS) to validate consistency across languages and tasks.
minor comments (1)
  1. [Abstract] Abstract: The phrase “strong performance” is used without any supporting numbers; adding one or two key quantitative highlights would improve the summary.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their valuable comments, which help improve the clarity and rigor of our work. Below, we provide detailed responses to each major comment and indicate the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (Dataset): No inter-annotator agreement statistics (e.g., Krippendorff’s alpha or pairwise agreement rates) are reported for the 128,754 annotations. This is load-bearing because the reliability of these labels directly determines the validity of both the reward optimization and the performance claims for SQ-LLM.

    Authors: We fully agree that inter-annotator agreement is crucial for establishing the reliability of the SpeechEval dataset. Although not reported in the initial submission, we have since computed these statistics on the annotations. In the revised manuscript, we will report Krippendorff’s alpha values (which indicate substantial agreement) and pairwise agreement rates for each task. This addition will directly support the validity of our reward optimization and performance claims. revision: yes

  2. Referee: [§5] §5 (Experiments): The results assert strong performance across tasks and languages but supply no quantitative metrics, baseline comparisons (e.g., against PESQ, MOS predictors, or prior LLM judges), statistical tests, or ablation studies isolating the contribution of chain-of-thought and reward optimization.

    Authors: We appreciate this observation and acknowledge that the experimental section would benefit from greater detail. In the revision, we will include specific quantitative metrics such as accuracy and correlation coefficients for each task and language. We will add comparisons to relevant baselines including PESQ, traditional MOS predictors, and existing LLM judges. Additionally, we will report statistical tests (e.g., paired t-tests) and ablation studies demonstrating the impact of chain-of-thought reasoning and reward optimization on SQ-LLM's performance. revision: yes

  3. Referee: [Methods] Methods: No details are given on annotation guidelines, rater screening procedures, or any post-hoc correlation of SpeechEval labels with established objective metrics (PESQ, STOI, or human MOS) to validate consistency across languages and tasks.

    Authors: We agree that providing these details is essential for reproducibility and validation. We will expand the Methods section to include the full annotation guidelines used for each task, the criteria and procedures for screening and selecting raters (including any qualification tests), and post-hoc correlation analyses between SpeechEval annotations and objective metrics such as PESQ, STOI, as well as available human MOS scores. These correlations will be presented across languages and tasks to demonstrate consistency. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation rests on new data and standard adaptation

full rationale

The paper collects a new multilingual dataset (SpeechEval) with 128,754 annotations across four tasks, then trains SQ-LLM via chain-of-thought and reward optimization on that resource. Experimental performance is reported on the resulting model without any quoted step that reduces a claimed prediction or uniqueness result to a fitted parameter, self-citation chain, or definitional tautology. No equations or load-bearing premises in the abstract or described structure equate outputs to inputs by construction; the central claim therefore remains independent of the patterns that would trigger a positive circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review conducted from abstract only; no explicit free parameters, invented entities, or non-standard axioms are stated. The work implicitly assumes standard LLM fine-tuning can transfer to perceptual speech judgments when given appropriate data.

axioms (1)
  • domain assumption Human annotations on speech quality can be collected at scale and used as reliable training signals for LLMs.
    This premise underpins the creation and use of the SpeechEval dataset for training SQ-LLM.

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Forward citations

Cited by 3 Pith papers

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

  1. NVBench: A Benchmark for Speech Synthesis with Non-Verbal Vocalizations

    cs.SD 2026-04 unverdicted novelty 7.0

    NVBench provides a standardized bilingual benchmark and evaluation protocol for assessing non-verbal vocalization generation, placement, and salience in text-to-speech systems.

  2. JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructions

    eess.AS 2026-05 unverdicted novelty 6.0

    JASTIN is an instruction-driven audio evaluation system that achieves state-of-the-art correlation with human ratings on speech, sound, music, and out-of-domain tasks without task-specific retraining.

  3. TTS-PRISM: A Perceptual Reasoning and Interpretable Speech Model for Fine-Grained Diagnosis

    cs.CL 2026-04 unverdicted novelty 6.0

    TTS-PRISM defines a 12-dimensional perceptual schema, builds a targeted diagnostic dataset via adversarial synthesis and expert labels, and tunes an end-to-end model that outperforms generalist LLMs in human alignment...

Reference graph

Works this paper leans on

57 extracted references · 57 canonical work pages · cited by 3 Pith papers · 2 internal anchors

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    Qwen2-Audio Technical Report

    Qwen2-audio technical report.arXiv preprint arXiv: 2407.10759. Erica Cooper and Junichi Yamagishi. 2021. How do voices from past speech synthesis challenges com- pare today?arXiv preprint arXiv:2105.02373. DeepSeek-AI. 2025. Deepseek-r1: Incentivizing rea- soning capability in llms via reinforcement learning. Preprint, arXiv:2501.12948. Michael Denkowski ...

    work page internal anchor Pith review Pith/arXiv arXiv 2021

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    End-to-end anti-spoofing with rawnet2. InICASSP 2021-2021 IEEE International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP), pages 6369–6373. IEEE. 10 Takaaki Saeki and Detai Xin and Wataru Nakata and Tomoki Koriyama and Shinnosuke Takamichi and Hi- roshi Saruwatari. 2022. UTMOS: UTokyo-SaruLab System for V oiceMOS Challenge 2022. InInte...

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    Qwen2.5-Omni Technical Report

    Uncertainty-Aware Mean Opinion Score Pre- diction. InInterspeech 2024, pages 1215–1219. Siyin Wang, Wenyi Yu, Xianzhao Chen, Xiaohai Tian, Jun Zhang, Lu Lu, Yu Tsao, Junichi Yamagishi, Yux- uan Wang, and Chao Zhang. 2025c. QualiSpeech: A speech quality assessment dataset with natural lan- guage reasoning and descriptions. InProceedings of the 63rd Annual ...

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  4. [4]

    Overall Quality Score: 2

    work page

  5. [5]

    Production Quality Intelligibility Score: 2 Distortion Score: 2 Distortion Type: background noise;timbre & quality Distortion Duration: appeared between 0 s - 5 s (across entire recording) Distortion Degree: Noticeable Distortion Description: rough and hissing Speech Rate: suitable Dynamic Range Score: 3

    work page

  6. [6]

    Content Enjoyment Emotional Impact Score: 2 Emotional Type: Neutral Artistic Expression Score:2 Subjective Experience Score:2 Gender: female Age: middle-aged Tone Description: The voice sounds rough and hissing

    work page

  7. [7]

    Objectively, intelligibility is poor, with noticeable background noise and timbre distortion throughout the 0-5 s duration, creating a rough, hissing effect

    Detailed Description The speech has significant quality issues, falling short in clarity and naturalness. Objectively, intelligibility is poor, with noticeable background noise and timbre distortion throughout the 0-5 s duration, creating a rough, hissing effect. The speech rate is suitable, and dynamics are moderately consistent. Subjectively, the female...

    work page

  8. [9]

    From an objective standpoint, intelligibility is subpar, with audible background noise and timbre distortion spanning the 0-5 second range, producing a coarse, sibilant effect

    The speech exhibits considerable quality shortcomings, primarily in clarity and natural delivery. From an objective standpoint, intelligibility is subpar, with audible background noise and timbre distortion spanning the 0-5 second range, producing a coarse, sibilant effect. The speech rate is well-paced, and dynamic consistency is moderate. Subjectively, ...

    work page

  9. [10]

    Objectively, it’s hard to understand due toconstant background noise and a distorted tone (0-5s), which adds a rough, hissing quality

    The speech has clear quality issues, struggling with both clarity and natural flow. Objectively, it’s hard to understand due toconstant background noise and a distorted tone (0-5s), which adds a rough, hissing quality. The speed is good, and the volume changes are fairly even. Subjectively, the middle-aged female voice sounds flat and unemotional, failing...

    work page

  10. [11]

    Objective Evaluation Score: 5

    work page

  11. [12]

    Production Quality Intelligibility Score: 5 Distortion Score:5 Distortion Type: None Distortion Duration: None Distortion Degree: None Distortion Description: None Speech Rate: suitable Dynamic Range Score: 4

    work page

  12. [13]

    Content Enjoyment Emotional Impact Score: 4 Emotional Type: Surprise Artistic Expression Score:4 Subjective Experience Score:5 Gender: male Age: middle-aged Tone Description: The voice sounds bright and full

    work page

  13. [14]

    Objectively, it excels in intelligibility and lacks any distortion, with a suitable speech rate and smooth dynamics, though volume consistency could be slightly improved

    Detailed Description The speech demonstrates excellent overall quality, delivering a highly polished performance. Objectively, it excels in intelligibility and lacks any distortion, with a suitable speech rate and smooth dynamics, though volume consistency could be slightly improved. From a subjective perspective, the male middle-aged speaker conveys a br...

    work page

  14. [16]

    Objectively, it achieves high intelligibility with no distortion, maintaining an appropriate pace and fluid dynamics, though minor volume fluctuations could be addressed

    The speech exhibits outstanding quality, presenting a refined and professional delivery. Objectively, it achieves high intelligibility with no distortion, maintaining an appropriate pace and fluid dynamics, though minor volume fluctuations could be addressed. Subjectively, the middle- aged male speaker projects a vibrant, resonant tone infused with a hint...

    work page

  15. [17]

    On a technical level, clarity is excellent, with no audible distortion, and the pacing and flow are well-managed, though slight volume adjustments could enhance consistency

    This speech is of exceptional quality, showcasing a polished and articulate performance. On a technical level, clarity is excellent, with no audible distortion, and the pacing and flow are well-managed, though slight volume adjustments could enhance consistency. From a listener’s perspective, the speaker— a middle-aged man— delivers a warm, rich tone with...

    work page

  16. [18]

    surprise

    This speech demonstrates remarkable quality, featuring a highly professional and engaging delivery. Technically, it offers excellent intelligibility without distortion, with a well-modulated speech rate and smooth dynamics, though volume uniformity could be fine-tuned. From a subjective standpoint, the middle-aged male speaker’s lively, well-rounded tone,...

    work page

  17. [19]

    Overall Quality A is better than B

    work page

  18. [20]

    Speech Rate: A and B are similar in this aspect Dynamic Range: A and B are similar in this aspect

    Production quality Intelligibility: A is better than B Distortion: A is better than B A Type: None Duration: None Degree: None Description: None B Type: timbre & quality Duration: 0 -3.7s (throughout the recording) Degree: extremely slight Description: Extremely slight rough. Speech Rate: A and B are similar in this aspect Dynamic Range: A and B are simil...

    work page

  19. [21]

    Content Enjoyment Emotional Impact: A and B are similar in this aspect A Type: Surprise B Type: Neutral Artistic Expression: A and B are similar in this aspect Subjective Experience: A and B are similar in this aspect A Gender: female Age: middle-aged Tone Description: crisp and bright B Gender: male Age: middle-aged Tone Description: mellow and full

    work page

  20. [22]

    Detailed Description Overall, Sample A demonstrates superior quality compared to Sample B. Objectively, Sample A excels in intelligibility, with clearer speech and no reported distortions, while Sample B exhibits an extremely slight timbre and quality distortion (0–3.7 s), perceived as a faint roughness. Both samples share similar speech rates and dynamic...

    work page

  21. [24]

    From an objective standpoint, Sample A delivers clearer speech with no distortions, whereas Sample B has a subtle roughness in timbre (0– 3.7s)

    In this comparison, Sample A emerges as the stronger performer. From an objective standpoint, Sample A delivers clearer speech with no distortions, whereas Sample B has a subtle roughness in timbre (0– 3.7s). Both samples maintain similar speech rates and dynamic ranges. Subjectively, their emotional impact (Sample A: surprise; Sample B: neutral), artisti...

    work page

  22. [25]

    Overall Quality A and B are similar in this aspect

    work page

  23. [26]

    B Type: background noise Duration: 0 -5s(throughout) Degree: slight Description: The sound has a slight background noise

    Production quality Intelligibility: A and B are similar in this aspect Distortion: A and B are similar in this aspect A Type: artifacts Duration: 0 -5s(throughout) Degree: slight Description: The sound is a little distorted. B Type: background noise Duration: 0 -5s(throughout) Degree: slight Description: The sound has a slight background noise. Speech Rat...

    work page

  24. [27]

    B Gender: female Age: 22s Tone Description: low and hollow

    Content Enjoyment Emotional Impact: A is better than B A Type: Neutral B Type: Neutral Artistic Expression: A and B are similar in this aspect Subjective Experience: A is better than B A Gender: female Age: 25s Tone Description: clear and light. B Gender: female Age: 22s Tone Description: low and hollow

    work page

  25. [28]

    Detailed Description Overall, Samples A and B demonstrate comparable quality, though subtle differences exist in technical and subjective aspects. Objectively, both samples exhibit similar intelligibility and dynamic range, but Sample A has a slight artifact distortion (0-5 s, slight degree), slightly affecting clarity, while Sample B has faint background...

    work page

  26. [30]

    While Samples A and B share many technical similarities, subtle distinctions influence their overall quality. Both exhibit strong intelligibility and dynamic range, but Sample A has minimal artifact distortion (0-5 s), slightly reducing clarity, whereas Sample B contains faint background noise (0-5 s). Sample A’s well-paced speech enhances understanding. ...

    work page

  27. [31]

    Objectively, intelligibility and dynamic range are equivalent, though Sample A has faint artifact distortion (0-5 s), and Sample B contains minimal background noise (0-5 s)

    A comparative analysis reveals that Samples A and B are closely matched in quality, differing only in subtle ways. Objectively, intelligibility and dynamic range are equivalent, though Sample A has faint artifact distortion (0-5 s), and Sample B contains minimal background noise (0-5 s). Sample A’s optimal speech rate improves listener comprehension. Subj...

    work page

  28. [32]

    Improvement Dimensions expressiveness;speech rate

    work page

  29. [33]

    Improve expressiveness by incorporating emotional variation and tonal dynamics

    Detailed Description Adjust the speech rate to a more natural pace to enhance clarity. Improve expressiveness by incorporating emotional variation and tonal dynamics

    work page

  30. [35]

    Enhance expressiveness through varied intonation and emotional inflection

    Moderate the speech rate for better naturalness and comprehension. Enhance expressiveness through varied intonation and emotional inflection. Minimize distortion to achieve cleaner, more lifelike speech output

    work page

  31. [36]

    Incorporate more dynamic vocal expression to enhance engagement

    Fine-tune the speech rate to improve naturalness and listener comfort. Incorporate more dynamic vocal expression to enhance engagement. Eliminate distortion artifacts for a cleaner and more polished output

    work page

  32. [37]

    Enhance vocal expressiveness through varied pitch and emotional modulation

    Adjust the speech rate to a more natural and listener-friendly tempo. Enhance vocal expressiveness through varied pitch and emotional modulation. Reduce distortion to ensure smoother and more intelligible speech

    work page

  33. [38]

    Improvement Dimensions expressiveness;emotional impact;noise reduction

    work page

  34. [39]

    Reduce background noise for cleaner audio quality

    Detailed Description Enhance emotional expressiveness to make the delivery more engaging. Reduce background noise for cleaner audio quality. Adjust speech rate to a more natural pace while maintaining clarity

    work page

  35. [41]

    Eliminate background noise for improved clarity

    Incorporate greater emotional variation to enhance engagement. Eliminate background noise for improved clarity. Slightly increase the speech rate for a more natural flow without sacrificing intelligibility

    work page

  36. [42]

    Clean up background noise for optimal audio clarity

    Strengthen emotional expression to make the speech more captivating. Clean up background noise for optimal audio clarity. Adjust the speech rate to a more natural tempo while preserving intelligibility

    work page

  37. [43]

    Improvement Dimensions dynamic range;intelligibility

    work page

  38. [44]

    Improve dynamic range consistency for smoother volume transitions

    Detailed Description Enhance intelligibility by reducing voice drops and missing segments. Improve dynamic range consistency for smoother volume transitions

    work page

  39. [46]

    Stabilize dynamic range for more uniform volume transitions

    Minimize voice dropouts and missing segments to enhance intelligibility. Stabilize dynamic range for more uniform volume transitions. Refine distortion management to reduce speech interruptions

    work page

  40. [47]

    Adjust dynamic range for steadier volume levels

    Address voice gaps and dropouts to improve intelligibility. Adjust dynamic range for steadier volume levels. Optimize distortion control to ensure smoother speech continuity

    work page

  41. [48]

    Fine-tune dynamic range to achieve smoother audio transitions

    Reduce speech dropouts and missing segments for better intelligibility. Fine-tune dynamic range to achieve smoother audio transitions. Enhance distortion mitigation to prevent speech breaks. Figure 12: Suggestion example for improving a low-quality speech sample

    work page

  42. [49]

    Improvement Dimensions emotional impact;frequency balance;intelligibility

    work page

  43. [50]

    Adjust frequency balance to improve voice clarity

    Detailed Description Enhance emotional expressiveness to engage listeners. Adjust frequency balance to improve voice clarity. Optimize intelligibility for better comprehension

    work page

  44. [52]

    Adjust tonal balance to refine voice clarity

    Increase emotional variation to enhance engagement. Adjust tonal balance to refine voice clarity. Boost intelligibility for better listener comprehension

    work page

  45. [53]

    Optimize frequency distribution for improved clarity

    Incorporate more expressive vocal modulation. Optimize frequency distribution for improved clarity. Strengthen intelligibility to aid listener perception

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  46. [54]

    Modify frequency settings to enhance voice clarity

    Add more dynamic vocal expression to engage the audience. Modify frequency settings to enhance voice clarity. Increase intelligibility for smoother listener comprehension

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  47. [55]

    Improvement Dimensions None

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  48. [56]

    Detailed Description No specific improvements are needed as the speech performs optimally across all evaluated dimensions

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  49. [58]

    No immediate enhancements are required, as the speech excels in every assessed aspect

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  50. [59]

    Improvement Dimensions Expressiveness

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  51. [60]

    Detailed Description To enhance expressiveness: Incorporate subtle vocal inflections to add emotional depth adjust pacing slightly to emphasize key phrases for greater engagement

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  52. [61]

    Alternative Description

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    Fine-tune pacing to highlight important phrases for better engagement

    To improve expressiveness: Introduce gentle vocal modulations to enrich emotional tone. Fine-tune pacing to highlight important phrases for better engagement. Vary pitch subtly to prevent a monotonous effect

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  54. [63]

    Adjust rhythm slightly to underscore key moments

    To enhance expressiveness: Employ subtle pitch variations to inject emotional depth. Adjust rhythm slightly to underscore key moments. Experiment with tonal shifts to prevent a flat presentation

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  55. [64]

    A and B are similar

    To refine expressiveness: Integrate delicate vocal inflections for emotional texture. Optimize pacing to draw attention to pivotal phrases. Vary tone slightly to avoid a monotonous effect. Figure 13: Suggestion example for refining a high-quality speech sample. 16 Sub-dimensions Annotation Criteria Overall Quality 1 point (Extremely Poor)Basic standard fo...

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  56. [65]

    G ive a numerical score (0 to 10) for Answer_2 based on its quality relative to Answer_1

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  57. [66]

    Speech Rate

    D o not score Answer_1. Use it only as the gold reference. Output Format: - Explanation: <Your reasoning here> - Score: <A number from 0 to 10> Speech Quality Assessment API Prompt Figure 18: Speech Quality Assessment API Prompt. Task: Evaluate the following synthesized speech based on the eight dimensions below. For each dimension, provide a score from 1...

    work page arXiv 2025