TraceAV-Bench is the first benchmark for multi-hop trajectory reasoning over long audio-visual videos, showing top models reach only 51-68% accuracy with substantial room for improvement.
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Qwen3-ASR Technical Report
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In this report, we introduce Qwen3-ASR family, which includes two powerful all-in-one speech recognition models and a novel non-autoregressive speech forced alignment model. Qwen3-ASR-1.7B and Qwen3-ASR-0.6B are ASR models that support language identification and ASR for 52 languages and dialects. Both of them leverage large-scale speech training data and the strong audio understanding ability of their foundation model Qwen3-Omni. We conduct comprehensive internal evaluation besides the open-sourced benchmarks as ASR models might differ little on open-sourced benchmark scores but exhibit significant quality differences in real-world scenarios. The experiments reveal that the 1.7B version achieves SOTA performance among open-sourced ASR models and is competitive with the strongest proprietary APIs while the 0.6B version offers the best accuracy-efficiency trade-off. Qwen3-ASR-0.6B can achieve an average TTFT as low as 92ms and transcribe 2000 seconds speech in 1 second at a concurrency of 128. Qwen3-ForcedAligner-0.6B is an LLM based NAR timestamp predictor that is able to align text-speech pairs in 11 languages. Timestamp accuracy experiments show that the proposed model outperforms the three strongest force alignment models and takes more advantages in efficiency and versatility. To further accelerate the community research of ASR and audio understanding, we release these models under the Apache 2.0 license.
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2026 30representative citing papers
FLARE is a new benchmark with 399 long videos, 87k multimodal clips, and 275k user-style queries for testing audiovisual retrieval under caption and query regimes.
A new multi-accent long-form call-center dialogue dataset for English ASR evaluation shows substantial performance variation across accents and segmentation methods.
Talker-T2AV achieves better lip-sync accuracy, video quality, and audio quality than dual-branch baselines by separating high-level shared autoregressive modeling from modality-specific low-level diffusion refinement in a joint audio-video generation framework.
LLM decoders in speech recognition show no racial bias amplification and fewer repetition hallucinations under degradation than Whisper, with audio encoder design mattering more than model scale for fairness and robustness.
AST enables seamless speech editing by latent recomposition on pre-trained TTS models plus adaptive weak fact guidance, plus a new dataset and WDTW metric, claiming 70% WER reduction and better temporal consistency without training.
Phoneme-based interfaces match or surpass projector-based ones for LLM ASR, especially in low-resource languages, and a BPE-phoneme hybrid offers additional improvements.
YingMusic-Singer-Plus is a diffusion model for singing voice synthesis that preserves melody from a reference clip while allowing flexible lyric changes without manual alignment, outperforming Vevo2 and introducing the LyricEditBench benchmark.
SQuTR aggregates 37k queries from six text retrieval datasets, synthesizes speech from 200 speakers, adds 17 noise categories at varying SNR, and shows that even large retrieval models degrade sharply under extreme acoustic noise.
MemoryCard organizes long videos into self-contained topic-aware Memory Cards that improve long-video QA accuracy by up to 21.8% relative under fixed visual-token budgets.
LaSR improves context-aware terminology recognition in speech LLMs by aligning latent CoT supervision on acoustic regions and introducing latent reasoning periods, shown on a new academic corpus to outperform standard fine-tuning without added latency.
A data pipeline, 14-dimension benchmark, and decoupled fine-tuning model are presented to advance fine-grained multi-dimensional speech understanding in LLMs.
VocalParse applies interleaved and Chain-of-Thought prompting to a Large Audio Language Model to jointly transcribe lyrics, melody and word-note alignments, achieving state-of-the-art results on multiple singing datasets.
Current audio-language models fail to use clinical multimodal context for dysarthric speech recognition, but context-aware LoRA fine-tuning delivers large accuracy gains on the SAP dataset.
LaDA-Band applies discrete masked diffusion with dual-track conditioning and progressive training to generate vocal-to-accompaniment tracks that improve acoustic authenticity, global coherence, and dynamic orchestration over prior baselines.
ASPIRin decouples speaking timing from token content via binary action space projection and applies GRPO with rule-based rewards to optimize interactivity in SLMs without semantic collapse or repetition.
A multi-stage training method for LLM-based ASR uses new entropy allocation metrics to achieve competitive benchmark performance with 2.3B parameters while mitigating hallucinations via better encoder-LLM decoupling.
A three-stage synthetic data pipeline generates 8800 doctor-patient conversations totaling 1.3k hours of audio and LLM-produced SOAP notes, with evaluation showing cascaded transcription-then-summarization models outperform end-to-end audio models.
StepAudio 2.5 is a unified audio-language foundation model that reaches state-of-the-art results on ASR, TTS, and realtime interaction by using task-tailored RLHF on a shared backbone.
A survey of Large Audio Language Models that establishes a taxonomy of trustworthiness vulnerabilities and proposes a Defense-in-Depth roadmap for audio intelligence.
CRAFT introduces a query-conditioned pipeline with dynamic keyframe selection, ASR, and a hybrid critic loop that achieves top scores on MAGMaR 2026 for grounded multi-video question answering.
The authors introduce LLM-based semantic judgment and an agentic interaction loop that improves semantic fidelity and enables iterative corrections in automatic speech recognition beyond traditional WER.
Dolphin-CN-Dialect is a compact ASR model that boosts Chinese dialect accuracy through balanced sampling of rare dialects and character-level tokenization while staying smaller than recent open-source competitors.
The 2026 PVUW Challenge introduces a new audio track and evaluates top multimodal methods on challenging video datasets for pixel-level understanding.
citing papers explorer
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TraceAV-Bench: Benchmarking Multi-Hop Trajectory Reasoning over Long Audio-Visual Videos
TraceAV-Bench is the first benchmark for multi-hop trajectory reasoning over long audio-visual videos, showing top models reach only 51-68% accuracy with substantial room for improvement.
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FLARE: Full-Modality Long-Video Audiovisual Retrieval Benchmark with User-Simulated Queries
FLARE is a new benchmark with 399 long videos, 87k multimodal clips, and 275k user-style queries for testing audiovisual retrieval under caption and query regimes.
-
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.
-
Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling
Talker-T2AV achieves better lip-sync accuracy, video quality, and audio quality than dual-branch baselines by separating high-level shared autoregressive modeling from modality-specific low-level diffusion refinement in a joint audio-video generation framework.
-
Do LLM Decoders Listen Fairly? Benchmarking How Language Model Priors Shape Bias in Speech Recognition
LLM decoders in speech recognition show no racial bias amplification and fewer repetition hallucinations under degradation than Whisper, with audio encoder design mattering more than model scale for fairness and robustness.
-
AST: Adaptive, Seamless, and Training-Free Precise Speech Editing
AST enables seamless speech editing by latent recomposition on pre-trained TTS models plus adaptive weak fact guidance, plus a new dataset and WDTW metric, claiming 70% WER reduction and better temporal consistency without training.
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Phonemes vs. Projectors: An Investigation of Speech-Language Interfaces for LLM-based ASR
Phoneme-based interfaces match or surpass projector-based ones for LLM ASR, especially in low-resource languages, and a BPE-phoneme hybrid offers additional improvements.
-
YingMusic-Singer-Plus: Controllable Singing Voice Synthesis with Flexible Lyric Manipulation and Annotation-free Melody Guidance
YingMusic-Singer-Plus is a diffusion model for singing voice synthesis that preserves melody from a reference clip while allowing flexible lyric changes without manual alignment, outperforming Vevo2 and introducing the LyricEditBench benchmark.
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SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noise
SQuTR aggregates 37k queries from six text retrieval datasets, synthesizes speech from 200 speakers, adds 17 noise categories at varying SNR, and shows that even large retrieval models degrade sharply under extreme acoustic noise.
-
MemoryCard: Topic-Aware Multi-Modal Clue Compression for Long-Video Question Answering
MemoryCard organizes long videos into self-contained topic-aware Memory Cards that improve long-video QA accuracy by up to 21.8% relative under fixed visual-token budgets.
-
LaSR: Context-Aware Speech Recognition via Latent Reasoning
LaSR improves context-aware terminology recognition in speech LLMs by aligning latent CoT supervision on acoustic regions and introducing latent reasoning periods, shown on a new academic corpus to outperform standard fine-tuning without added latency.
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Towards Fine-Grained Multi-Dimensional Speech Understanding: Data Pipeline, Benchmark, and Model
A data pipeline, 14-dimension benchmark, and decoupled fine-tuning model are presented to advance fine-grained multi-dimensional speech understanding in LLMs.
-
VocalParse: Towards Unified and Scalable Singing Voice Transcription with Large Audio Language Models
VocalParse applies interleaved and Chain-of-Thought prompting to a Large Audio Language Model to jointly transcribe lyrics, melody and word-note alignments, achieving state-of-the-art results on multiple singing datasets.
-
When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition
Current audio-language models fail to use clinical multimodal context for dysarthric speech recognition, but context-aware LoRA fine-tuning delivers large accuracy gains on the SAP dataset.
-
LaDA-Band: Language Diffusion Models for Vocal-to-Accompaniment Generation
LaDA-Band applies discrete masked diffusion with dual-track conditioning and progressive training to generate vocal-to-accompaniment tracks that improve acoustic authenticity, global coherence, and dynamic orchestration over prior baselines.
-
ASPIRin: Action Space Projection for Interactivity-Optimized Reinforcement Learning in Full-Duplex Speech Language Models
ASPIRin decouples speaking timing from token content via binary action space projection and applies GRPO with rule-based rewards to optimize interactivity in SLMs without semantic collapse or repetition.
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Rethinking Entropy Allocation in LLM-based ASR: Understanding the Dynamics between Speech Encoders and LLMs
A multi-stage training method for LLM-based ASR uses new entropy allocation metrics to achieve competitive benchmark performance with 2.3B parameters while mitigating hallucinations via better encoder-LLM decoupling.
-
Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization
A three-stage synthetic data pipeline generates 8800 doctor-patient conversations totaling 1.3k hours of audio and LLM-produced SOAP notes, with evaluation showing cascaded transcription-then-summarization models outperform end-to-end audio models.
-
StepAudio 2.5 Technical Report
StepAudio 2.5 is a unified audio-language foundation model that reaches state-of-the-art results on ASR, TTS, and realtime interaction by using task-tailored RLHF on a shared backbone.
-
A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook
A survey of Large Audio Language Models that establishes a taxonomy of trustworthiness vulnerabilities and proposes a Defense-in-Depth roadmap for audio intelligence.
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CRAFT: Critic-Refined Adaptive Key-Frame Targeting for Multimodal Video Question Answering
CRAFT introduces a query-conditioned pipeline with dynamic keyframe selection, ASR, and a hybrid critic loop that achieves top scores on MAGMaR 2026 for grounded multi-video question answering.
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Interactive ASR: Towards Human-Like Interaction and Semantic Coherence Evaluation for Agentic Speech Recognition
The authors introduce LLM-based semantic judgment and an agentic interaction loop that improves semantic fidelity and enables iterative corrections in automatic speech recognition beyond traditional WER.
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Dolphin-CN-Dialect: Where Chinese Dialects Matter
Dolphin-CN-Dialect is a compact ASR model that boosts Chinese dialect accuracy through balanced sampling of rare dialects and character-level tokenization while staying smaller than recent open-source competitors.
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Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding
The 2026 PVUW Challenge introduces a new audio track and evaluates top multimodal methods on challenging video datasets for pixel-level understanding.
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Pushing the Limits of On-Device Streaming ASR: A Compact, High-Accuracy English Model for Low-Latency Inference
A quantized int4 version of Nemotron ASR runs faster than real-time on CPU at 8.20% WER and 0.67 GB size, setting a new efficiency point for on-device streaming speech recognition.
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PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory
PASK introduces the DD-MM-PAS paradigm for streaming proactive agents with intent-aware detection, hybrid memory modeling, and a new real-world benchmark where the IntentFlow model matches top LLMs on latency while finding deeper intents.
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LLMs and Speech: Integration vs. Combination
Tight integration of acoustic models with LLMs for ASR is ablated against shallow fusion across label units, fine-tuning strategies, LLM sizes, and joint CTC decoding to mitigate hallucinations.
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2nd of the 5th PVUW MeViS-Audio Track: ASR-SaSaSa2VA
ASR-SaSaSa2VA turns audio into text via ASR then feeds it to pre-trained referring video segmentation models, achieving 80.7 and second place in the 5th PVUW MeViS-v2-Audio track.
- NIM4-ASR: Towards Efficient, Robust, and Customizable Real-Time LLM-Based ASR
- Harf-Speech: A Clinically Aligned Framework for Arabic Phoneme-Level Speech Assessment