arXiv:2606.13507v1 Announce Type: new Abstract: Large-scale mined corpora provide abundant training data for end-to-end speech-to-speech translation (S2ST) but may contain noise, misalignment, and semantic errors. Filtering noisy data is crucial to maintain robust speech translation performance. We study how to train an audio-language model to make keep/drop decisions on paired speech directly from audio. To obtain reliable supervision without manual labels, we adopt a scalable two-stage Rank-to-Distill strategy. A lightweight ranker generates keep/drop pseudo-labels from noisy speech pairs, t
The proliferation of AI models reliant on vast datasets necessitates robust mechanisms to ensure data quality, especially for complex modalities like speech-to-speech translation.
Improving the quality of training data for speech-to-speech translation directly enhances the performance and reliability of AI systems, expanding their utility and accuracy in real-world applications.
This research introduces a novel, scalable method for efficiently filtering noisy speech-to-speech training data using audio-language models, promising more robust and accurate speech translation models.
- · AI developers (speech technology)
- · Cloud providers (AI services)
- · Multilingual communication platforms
- · Global businesses
- · Manual data annotation services
- · Models trained on unfiltered noisy data
Higher quality speech-to-speech translation becomes more accessible.
Improved translation accuracy could accelerate global information exchange and business operations.
More sophisticated and reliable AI agents and systems could emerge, capable of natural, cross-lingual communication.
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