HALAS is a human-annotated dataset of ASR hallucinations on unprocessed real audio that shows simple metrics outperform current detection methods at 81% ROC-AUC versus 53.1% F1.
Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual Speech Recognition Evaluation
10 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 10representative citing papers
Mel-LLM shows an LLM can achieve competitive ASR by directly ingesting pre-processed Mel spectrogram patches through a linear projection layer.
A new multi-accent long-form call-center dialogue dataset for English ASR evaluation shows substantial performance variation across accents and segmentation methods.
Audex unifies audio understanding and generation on a strong text MoE backbone with multi-stage SFT plus text-only Cascade RL, matching open SOTA audio scores while mostly retaining text capability.
TRADE augments multimodal Speech LLMs with a transducer branch for streaming ASR, reporting 6.71% WER offline and 8.40% streaming on the Open ASR Leaderboard from one checkpoint.
BlasBench supplies an Irish-aware normalizer and scoring harness that enables reproducible ASR comparisons and exposes a 33-43 point generalization gap for fine-tuned models versus 7-10 points for massively multilingual ones.
SURE is a new standardized framework for evaluating and training speech foundation models and Speech LLMs to improve comparability and reproducibility under realistic conditions.
Nemotron 3 Nano Omni is an efficient open multimodal model supporting audio, text, images, and video with reported accuracy gains and leading results on document understanding and long audio-video tasks.
Fusing multiple parallel pre-trained speech encoders into LLM-based ASR yields consistent performance gains across mono- and multilingual and diarized settings with limited added cost.
A cascaded SimulST system using Parakeet and Qwen 3.5 with adaptive black-box policies and RAG context achieves +5.82 XCOMET-XL improvement on En→De for IWSLT 2026.
citing papers explorer
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HALAS: A Human-Annotated Dataset of Hallucinations of Modern ASR Systems
HALAS is a human-annotated dataset of ASR hallucinations on unprocessed real audio that shows simple metrics outperform current detection methods at 81% ROC-AUC versus 53.1% F1.
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LLM can Read Spectrogram: Encoder-free Speech-Language Modeling
Mel-LLM shows an LLM can achieve competitive ASR by directly ingesting pre-processed Mel spectrogram patches through a linear projection layer.
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AppTek Call-Center Dialogues: A Multi-Accent Long-Form Benchmark for English ASR
A new multi-accent long-form call-center dialogue dataset for English ASR evaluation shows substantial performance variation across accents and segmentation methods.
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Unified Audio Intelligence Without Regressing on Text Intelligence
Audex unifies audio understanding and generation on a strong text MoE backbone with multi-stage SFT plus text-only Cascade RL, matching open SOTA audio scores while mostly retaining text capability.
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TRADE: Transducer-Augmented Decoder for Speech LLM
TRADE augments multimodal Speech LLMs with a transducer branch for streaming ASR, reporting 6.71% WER offline and 8.40% streaming on the Open ASR Leaderboard from one checkpoint.
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BlasBench: An Open Benchmark for Irish Speech Recognition
BlasBench supplies an Irish-aware normalizer and scoring harness that enables reproducible ASR comparisons and exposes a 33-43 point generalization gap for fine-tuned models versus 7-10 points for massively multilingual ones.
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A Unified and Reproducible Experimentation Framework for Speech Understanding
SURE is a new standardized framework for evaluating and training speech foundation models and Speech LLMs to improve comparability and reproducibility under realistic conditions.
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Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
Nemotron 3 Nano Omni is an efficient open multimodal model supporting audio, text, images, and video with reported accuracy gains and leading results on document understanding and long audio-video tasks.
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Speech Encoder Fusion for LLM-based Automatic Speech Recognition
Fusing multiple parallel pre-trained speech encoders into LLM-based ASR yields consistent performance gains across mono- and multilingual and diarized settings with limited added cost.
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MLLP-VRAIN UPV system for the IWSLT 2026 Simultaneous Speech Translation task
A cascaded SimulST system using Parakeet and Qwen 3.5 with adaptive black-box policies and RAG context achieves +5.82 XCOMET-XL improvement on En→De for IWSLT 2026.