HydraQE: New End-to-End Speech Translation Quality Estimation System
Researchers from OSU have developed HydraQE, an end-to-end, reference-free quality estimation system for speech translation built on a Qwen3-ASR backbone, accepted to the IWSLT 2026 shared task. The system combines hidden states across all backbone layers, uses three independent prediction heads trained on complementary supervision signals, and employs a curriculum learning strategy to compensate for scarce human-annotated data. HydraQE outperforms cascaded text-based baselines, demonstrating that end-to-end speech translation quality estimation can be competitive with traditional pipeline approaches.
HydraQE is an end-to-end, reference-free quality estimation (QE) system for speech translation submitted by Ohio State University (OSU) to the IWSLT 2026 Speech Translation Metrics shared task. Built on a Qwen3-ASR backbone, the system accepts source audio and a translation hypothesis as joint input, combining hidden states from all backbone layers via a learnable sparsemax scalar mix. These representations are then re-encoded by a lightweight bidirectional Transformer to enable full cross-modal interaction before being pooled into a shared embedding. Three independent prediction heads are trained on distinct supervision signals: human direct assessment (DA) annotations, MetricX-24 pseudo-labels, and xCOMET pseudo-labels. To address the limited availability of human-annotated training data, the system is trained on synthetically corrupted examples and silver pseudo-labeled machine translation outputs using a curriculum that progressively shifts toward human-annotated data. Experimental results show HydraQE outperforms cascaded text-based baselines and prior direct speech QE systems, supporting the viability of end-to-end approaches for speech translation quality estimation.
What's missing
The generalizability of HydraQE across language pairs, domains, and speech conditions beyond those evaluated in the shared task remains an open question. The reliance on pseudo-labels from MetricX-24 and xCOMET introduces potential label noise whose downstream impact on model reliability is not fully characterized.
What different sources said
- arXiv cs.CLCenter
HydraQE: OSU's Submission for the IWSLT 2026 Speech Translation Metrics Shared Task
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