Better Late Than Never: Meta-Evaluation of Latency Metrics for Simultaneous Speech-to-Text Translation
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| Format: | Preprint |
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2025
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| _version_ | 1866915838872780800 |
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| author | Polák, Peter Papi, Sara Bentivogli, Luisa Bojar, Ondřej |
| author_facet | Polák, Peter Papi, Sara Bentivogli, Luisa Bojar, Ondřej |
| contents | Simultaneous speech-to-text translation systems must balance translation quality with latency. Although quality evaluation is well established, latency measurement remains a challenge. Existing metrics produce inconsistent results, especially in short-form settings with artificial presegmentation. We present the first comprehensive meta-evaluation of latency metrics across language pairs and systems. We uncover a structural bias in current metrics related to segmentation. We introduce YAAL (Yet Another Average Lagging) for a more accurate short-form evaluation and LongYAAL for unsegmented audio. We propose SoftSegmenter, a resegmentation tool based on soft word-level alignment. We show that YAAL and LongYAAL, together with SoftSegmenter, outperform popular latency metrics, enabling more reliable assessments of short- and long-form simultaneous speech translation systems. We implement all artifacts within the OmniSTEval toolkit: https://github.com/pe-trik/OmniSTEval. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_17349 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Better Late Than Never: Meta-Evaluation of Latency Metrics for Simultaneous Speech-to-Text Translation Polák, Peter Papi, Sara Bentivogli, Luisa Bojar, Ondřej Computation and Language Artificial Intelligence Simultaneous speech-to-text translation systems must balance translation quality with latency. Although quality evaluation is well established, latency measurement remains a challenge. Existing metrics produce inconsistent results, especially in short-form settings with artificial presegmentation. We present the first comprehensive meta-evaluation of latency metrics across language pairs and systems. We uncover a structural bias in current metrics related to segmentation. We introduce YAAL (Yet Another Average Lagging) for a more accurate short-form evaluation and LongYAAL for unsegmented audio. We propose SoftSegmenter, a resegmentation tool based on soft word-level alignment. We show that YAAL and LongYAAL, together with SoftSegmenter, outperform popular latency metrics, enabling more reliable assessments of short- and long-form simultaneous speech translation systems. We implement all artifacts within the OmniSTEval toolkit: https://github.com/pe-trik/OmniSTEval. |
| title | Better Late Than Never: Meta-Evaluation of Latency Metrics for Simultaneous Speech-to-Text Translation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2509.17349 |