Qwen3-ASR (AI model): news timeline & CVEs

HINTT Submission to the 2nd MLC-SLM Challenge: Comparing Cascaded and Unified Approaches to Diarization and ASR

HINTT's cascaded diarization and Qwen3-ASR pipeline beat a unified speech LLM on MLC-SLM Task 1.

HINTT submitted a multilingual speaker-attributed ASR system to the 2nd MLC-SLM challenge, which requires determining who spoke when and what was said. The submitted cascaded pipeline combines a fine-tuned DiariZen diarization model, a fine-tuned Qwen3-ASR model, and LLM-based generative error correction. A unified VibeVoice-ASR model was fine-tuned on the same official training data for comparison, without external data or pseudo-labels. Under Task 1 conditions, the cascaded system was more reliable, while unified speech LLMs remain a future direction.

arXiv cs.AI / cs.LG / cs.CLOct 6, 09:57 UTC · 3d agoAI research1

Pruned CTC for Memory-Efficient Large-Vocabulary ASR Training

Pruned CTC restricts alignment computation to target tokens and blank, cutting ASR training memory 5.1x for 180K-vocabulary LLMs with minimal accuracy loss.

Pruned CTC restricts CTC alignment computation to the small subset of target tokens plus blank while retaining full-vocabulary normalization, provably equivalent to standard CTC in loss and gradients. With a Zipformer-M encoder and 180K vocabulary, it reduces full-step memory 5.1x with only 17% step-time overhead and matches standard CTC accuracy across three corpora. The authors build LLM-CTC, adapting pretrained LLMs (Qwen3, 0.6B to 32B) for non-autoregressive and streaming ASR, staying within 7% relative WER of LLM-CE while recognizing 7-10x faster.

Hugging Face daily papersSep 26, 20:00 UTC · 12d agoAI research

Qwen3-ASR

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HINTT Submission to the 2nd MLC-SLM Challenge: Comparing Cascaded and Unified Approaches to Diarization and ASR

Pruned CTC for Memory-Efficient Large-Vocabulary ASR Training