Benchmarking Speech-to-Speech Translation Models
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Abstract (arXiv)
Speech-to-speech translation (S2ST) has advanced rapidly, but offline evaluation lacks a unified protocol: studies report non-overlapping metric subsets, preventing direct comparisons. We introduce COMPASS, a unified and reproducible benchmarking framework integrating 46 metrics across eight dimensions, and deploy it on 1,248 model-language configurations from FLEURS and CVSS, spanning cascaded and end-to-end architectures over ten language pairs. Architectures exhibit complementary strengths: best-vs-worst gaps exceed 30% on naturalness and speaker preservation but remain within a few points on translation quality, so single-metric rankings systematically misrepresent system quality. Correlation filtering reduces 46 metrics to 10 per direction, with three axes requiring different metrics across X→EN and EN→X (e.g., TER/UTMOS vs. ChrF++/NISQA-MOS); these subsets preserve rankings (Spearman's ρ>0.80) while cutting evaluation time by ≈ 2.5×. Human validation across dubbing, podcasts, and medical domains shows standalone MOS predictors fail to predict listener preference, while top domain-specific metrics correlate with human judgment (ρ ≥ 0.90). We release COMPASS as a foundation for domain-aware S2ST evaluation.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 37 of its 77 references that are in the map. First posted on arXiv 2 Jun 2026.
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References · 37
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- 2022UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022cited by 381
25 more
- 2021Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translationcited by 333
- 2023emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representationcited by 196
- 2021NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasetscited by 189
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- 2024IsoChronoMeter: A simple and effective isochronic translation evaluation metriccited by 1
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