HydraQE: New End-to-End Speech Translation Quality Estimation System
Researchers at Ohio State University developed HydraQE, an end-to-end quality estimation system for speech translation that works without reference translations. The system uses a Qwen3-ASR backbone combined with a lightweight Transformer and is trained on human annotations plus synthetic data. HydraQE outperforms existing cascaded text-based approaches, suggesting direct speech translation quality assessment is viable.
HydraQE is a reference-free quality estimation system designed to evaluate speech translation outputs directly from source audio and translation hypotheses. The system architecture combines hidden states from all backbone layers through a learnable sparsemax scalar mix, followed by re-encoding through a bidirectional Transformer to enable cross-modal interaction. To overcome limited human-annotated data, the researchers employed a curriculum learning approach that starts with synthetically corrupted examples and silver pseudo-labeled outputs before transitioning to human-annotated examples. The system uses three independent prediction heads trained on complementary supervision signals: human direct assessment annotations, MetricX-24 pseudo-labels, and xCOMET pseudo-labels. According to the authors, HydraQE demonstrates competitive performance compared to cascaded approaches, suggesting that end-to-end speech translation quality estimation is a viable alternative to traditional pipeline methods.
What's missing
The paper does not provide specific quantitative performance metrics (e.g., correlation scores with human judgments) or detailed comparisons with named baseline systems, making it difficult to assess the magnitude of improvement claimed. Additionally, the generalization of the approach to low-resource language pairs and real-world deployment considerations are not discussed.
What different sources said
- arXiv cs.CLCenter
HydraQE: OSU's Submission for the IWSLT 2026 Speech Translation Metrics Shared Task
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