arXiv:2605.30792v1 Announce Type: cross Abstract: Speech translation systems increasingly span speech-to-text translation (S2TT), speech-to-speech translation (S2ST), offline translation, and streaming generation, producing outputs that differ in modality, speech realization, and timing behavior. Existing evaluation practices assess important aspects such as translation quality, speech quality, and temporal quality, but these aspects are often evaluated under separate protocols, making it difficult to compare heterogeneous systems comprehensively. To address this gap, we present OpenSTBench, a
The proliferation of diverse speech translation systems necessitates a unified and comprehensive evaluation framework to effectively compare their performance and progress.
A standardized benchmark for speech translation will accelerate research and development, enabling clearer comparisons and driving innovation in AI-powered communication technologies.
The way speech translation systems are evaluated will become more holistic, moving beyond individual metrics to encompass modality, speech realization, and timing behavior.
- · AI researchers
- · Speech translation developers
- · Multimodal AI
- · Language technology companies
- · Systems with narrow evaluation focus
- · Fragmented evaluation protocols
OpenSTBench provides a new standard for assessing speech translation systems across various outputs.
This improved evaluation will lead to more robust and versatile speech translation models capable of handling diverse real-world scenarios.
Better speech translation could facilitate more seamless global communication and enhance accessibility across different languages and modalities.
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Read at arXiv cs.AI