arXiv:2606.15059v1 Announce Type: new 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 generate
The increasing sophistication and demand for real-time multilingual communication necessitate more robust and practical evaluation methods for simultaneous speech-to-speech translation systems.
Improved evaluation for long-form speech translation will accelerate the development and deployment of reliable real-time communication technologies, critical for global collaboration and accessibility.
The ability to accurately assess simultaneous speech-to-speech translation for continuous, long-form input will shift development focus from segmented speech to more practical, end-to-end applications.
- · AI developers
- · Multilingual communication platforms
- · International businesses
- · Global users
- · Legacy translation services
- · Systems focused only on short-form speech
More accurate and reliable real-time cross-lingual communication tools become available.
Reduced language barriers facilitate greater international collaboration in various sectors, from business to diplomacy.
The development of highly performant, real-time S2ST could lead to new forms of global digital interaction and potentially impact cultural exchange dynamically.
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