A Practical Evaluation Method for Long-Form Simultaneous Speech-to-Speech Translation
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Computer Science > Computation and Language
Title:A Practical Evaluation Method for Long-Form Simultaneous Speech-to-Speech Translation
Abstract:Simultaneous speech-to-speech translation (SimulS2ST) enables real-time cross-lingual communication, but existing evaluation has focused largely on short or pre-segmented speech rather than long-form, continuous input. Prior approaches are difficult to reproduce and make assumptions that do not hold for end-to-end systems. We present a practical evaluation method for long-form SimulS2ST. Given source speech, pre-segmented source transcripts, and reference translations, we run automatic speech recognition (ASR) and forced alignment on the generated target speech to recover token-level timestamps, then apply a sentence-embedding-based aligner to match the target text to its corresponding source sentences. This enables sentence-level computation of latency and quality metrics, including YAAL and xCOMET, which are then aggregated into final system-level scores. Experiments on representative SimulS2ST systems show that the method is effective in practice and reveal that current systems suffer from substantial latency accumulation on long speech.
| Comments: | Accepted to IWSLT 2026 Scientific Track |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.15059 [cs.CL] |
| (or arXiv:2606.15059v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.15059
arXiv-issued DOI via DataCite (pending registration)
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