# Towards Deep Contextual Reasoning from Broad Descriptions for ASR with Speech-LLM via Metadata-Driven Reasoning Chains

arXiv: https://arxiv.org/abs/2606.10838 · HTML: https://mlsanity.com/paper/2606.10838/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speech recognition models and training, one of 11 directions in Speech and audio language models.
- Builds on 29 of its 33 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Placed in

- Speech and audio language models / [Speech recognition models and training](https://mlsanity.com/speech/speech-recognition-models-and-training/)

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://mlsanity.com/paper/2305.02301/) (2023)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2410.19168/) (2024)
- [GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities](https://mlsanity.com/paper/2406.11768/) (2024)
- [Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models](https://mlsanity.com/paper/2503.02318/) (2025)
- [Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model](https://mlsanity.com/paper/2501.07246/) (2025)
- [DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment](https://mlsanity.com/paper/2507.02768/) (2025)
- [DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data](https://mlsanity.com/paper/2409.20007/) (2024)
- [SAKURA: On the Multi-hop Reasoning of Large Audio-Language Models Based on Speech and Audio Information](https://mlsanity.com/paper/2505.13237/) (2025)
- [OWSM v4: Improving Open Whisper-Style Speech Models via Data Scaling and Cleaning](https://mlsanity.com/paper/2506.00338/) (2025)
- [DeSTA: Enhancing Speech Language Models through Descriptive Speech-Text Alignment](https://mlsanity.com/paper/2406.18871/) (2024)
- [Can Large Audio-Language Models Truly Hear? Tackling Hallucinations with Multi-Task Assessment and Stepwise Audio Reasoning](https://mlsanity.com/paper/2410.16130/) (2024)
- [SlideSpeech: A Large-Scale Slide-Enriched Audio-Visual Corpus](https://mlsanity.com/paper/2309.05396/) (2023)
- [Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models](https://mlsanity.com/paper/2405.10025/) (2024)
- [BR-ASR: Efficient and Scalable Bias Retrieval Framework for Contextual Biasing ASR in Speech LLM](https://mlsanity.com/paper/2505.19179/) (2025)
- [Contextualized End-to-end Automatic Speech Recognition with Intermediate Biasing Loss](https://mlsanity.com/paper/2406.16120/) (2024)
- [SlideAVSR: A Dataset of Paper Explanation Videos for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2401.09759/) (2024)
- [SALSA: Speedy ASR-LLM Synchronous Aggregation](https://mlsanity.com/paper/2408.16542/) (2024)
- [Contextual Biasing of Named-Entities with Large Language Models](https://mlsanity.com/paper/2309.00723/) (2023)
- [Improving Domain-Specific ASR with LLM-Generated Contextual Descriptions](https://mlsanity.com/paper/2407.17874/) (2024)
- [Towards interfacing large language models with ASR systems using confidence measures and prompting](https://mlsanity.com/paper/2407.21414/) (2024)
- [M3AV: A Multimodal, Multigenre, and Multipurpose Audio-Visual Academic Lecture Dataset](https://mlsanity.com/paper/2403.14168/) (2024)
- [Speech Recognition Rescoring with Large Speech-Text Foundation Models](https://mlsanity.com/paper/2409.16654/) (2024)
- [Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing](https://mlsanity.com/paper/2501.13831/) (2025)
- [Phonetically-Augmented Discriminative Rescoring for Voice Search Error Correction](https://mlsanity.com/paper/2506.06117/) (2025)

## Abstract (arXiv)

Speech recognition often fails on rare, domain-specific terms and context-related named entities. Existing contextualization techniques typically bias decoding with keywords or phrase lists, which does not scale well or exploit deeper knowledge. We propose a training method that teaches a speech-LLM to use broad descriptions (e.g. from videos) as weak semantic priors to perform contextual reasoning grounded in the audio. We build 400 hours of reasoning-augmented speech data by pairing erroneous hypotheses with video metadata and LLM-generated reasoning explanations that justify context-driven corrections. We finetune the speech-LLM to perform chain-of-thought reasoning: generate an initial transcript, then reason over the context, and finally return a corrected transcript. On held-out YouTube-derived test sets, our approach reduces errors, with specific improvements on rare words and named entities, and lays groundwork for deeper contextual reasoning in speech recognition.