arXiv:2606.06047v1 Announce Type: new Abstract: Errors in speech translations reduce trustworthiness of Speech Translation (ST) systems and can have serious consequences. Yet currently there is no established methodology for evaluating confidence and quality estimation of speech translations. To initiate progress in this direction, we propose Speech Translation Error Labelling (STEL). We create an annotation protocol, a small authentic end-to-end evaluation dataset, and we analyse how existing text-only and speech-processing systems perform the STEL task. Our results show that text-only XCOMET
The increasing deployment of AI-powered speech translation systems necessitates robust error detection to ensure reliability, leading researchers to develop new methodologies for evaluation.
Improved error labelling for speech translation systems is crucial for enhancing the trustworthiness and real-world applicability of AI in critical communication scenarios, impacting user adoption and regulatory oversight.
The introduction of a standardized Speech Translation Error Labelling (STEL) protocol and evaluation dataset allows for more systematic assessment and improvement of speech translation quality.
- · AI developers
- · Speech translation users
- · Language service providers
- · AI ethics and safety researchers
- · Developers of unreliable ST systems
- · Companies relying on poor quality speech translation
More accurate and reliable speech translation systems will emerge as error detection improves.
Increased trust in speech translation technologies could accelerate their adoption in sensitive sectors like healthcare or legal services.
Standardized error evaluation could lead to regulatory frameworks for AI speech translation, similar to those for other safety-critical AI applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL