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    <abstract>Simultaneous speech translation (SimulST) systems must balance translation quality with response time, making latency measurement crucial for evaluating their real-world performance. However, there has been a longstanding belief that current metrics yield unrealistically high latency measurements in unsegmented streaming settings. In this paper, we investigate this phenomenon, revealing its root cause in a fundamental misconception underlying existing latency evaluation approaches. We demonstrate that this issue affects not only streaming but also segment-level latency evaluation across different metrics. Furthermore, we propose a modification to correctly measure computation-aware latency for SimulST systems, addressing the limitations present in existing metrics.</abstract>
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%0 Conference Proceedings
%T CA*: Addressing Evaluation Pitfalls in Computation-Aware Latency for Simultaneous Speech Translation
%A Xu, Xi
%A Xu, Wenda
%A Ouyang, Siqi
%A Li, Lei
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F xu-etal-2025-ca
%X Simultaneous speech translation (SimulST) systems must balance translation quality with response time, making latency measurement crucial for evaluating their real-world performance. However, there has been a longstanding belief that current metrics yield unrealistically high latency measurements in unsegmented streaming settings. In this paper, we investigate this phenomenon, revealing its root cause in a fundamental misconception underlying existing latency evaluation approaches. We demonstrate that this issue affects not only streaming but also segment-level latency evaluation across different metrics. Furthermore, we propose a modification to correctly measure computation-aware latency for SimulST systems, addressing the limitations present in existing metrics.
%R 10.18653/v1/2025.findings-naacl.393
%U https://aclanthology.org/2025.findings-naacl.393/
%U https://doi.org/10.18653/v1/2025.findings-naacl.393
%P 7062-7067

Markdown (Informal)

[CA*: Addressing Evaluation Pitfalls in Computation-Aware Latency for Simultaneous Speech Translation](https://aclanthology.org/2025.findings-naacl.393/) (Xu et al., Findings 2025)

• CA*: Addressing Evaluation Pitfalls in Computation-Aware Latency for Simultaneous Speech Translation (Xu et al., Findings 2025)

ACL

• Xi Xu, Wenda Xu, Siqi Ouyang, and Lei Li. 2025. CA*: Addressing Evaluation Pitfalls in Computation-Aware Latency for Simultaneous Speech Translation. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 7062–7067, Albuquerque, New Mexico. Association for Computational Linguistics.