qwen3-asr-ab7c4b09·2 events·first seen Aliases: Qwen3-ASR
Researchers adapted Whisper to a single dysarthric speaker using up to 100.8 hours of read speech and user corrections collected via a mobile app, reducing word error rate from a high baseline to 9.7%. Fine-tuning outperformed LoRA adaptation and the Qwen3-ASR foundation model in this personalized setting. The study demonstrates that speaker-specific fine-tuning of foundation ASR models can reach practical deployment quality for dysarthric users.
Researchers present AlignAtt4LLM, a simultaneous speech translation system for IWSLT 2026 covering English to German, Italian, and Chinese. The system cascades Qwen3-ASR for incremental transcription with Gemma-4 E4B-it for translation, applying a novel AlignAtt policy adapted for decoder-only LLMs that lack encoder-decoder cross-attention. Key contributions include explicit source span prompting, offline alignment head selection, and query/key capture to recover a usable attention-based read/write policy. The system outperforms IWSLT 2026 baselines for European language pairs in both low- and high-latency regimes.