# HearInContext: A Benchmark for Implicit Context in Speech Recognition

arXiv: https://arxiv.org/abs/2609.18680 · HTML: https://mlsanity.com/paper/2609.18680/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 28 references that are in the map.
- First posted on arXiv 16 Sep 2026.

## Builds on (most cited first, 22 of 22)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models](https://mlsanity.com/paper/2412.10117/) (2024)
- [Deep context: end-to-end contextual speech recognition](https://mlsanity.com/paper/1808.02480/) (2018)
- [FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs](https://mlsanity.com/paper/2407.04051/) (2024)
- [Qwen3-ASR Technical Report](https://mlsanity.com/paper/2601.21337/) (2026)
- [SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation](https://mlsanity.com/paper/2310.09424/) (2023)
- [Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition](https://mlsanity.com/paper/2407.04675/) (2024)
- [Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognition](https://mlsanity.com/paper/2306.16007/) (2023)
- [Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers](https://mlsanity.com/paper/2104.09426/) (2021)
- [SlideSpeech: A Large-Scale Slide-Enriched Audio-Visual Corpus](https://mlsanity.com/paper/2309.05396/) (2023)
- [SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability](https://mlsanity.com/paper/2308.03266/) (2023)
- [VIBEVOICE-ASR Technical Report](https://mlsanity.com/paper/2601.18184/) (2026)
- [FireRedASR2S: A State-of-the-Art Industrial-Grade All-in-One Automatic Speech Recognition System](https://mlsanity.com/paper/2603.10420/) (2026)
- [ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark](https://mlsanity.com/paper/2507.05727/) (2025)
- [Homophone Disambiguation Reveals Patterns of Context Mixing in Speech Transformers](https://mlsanity.com/paper/2310.09925/) (2023)
- [Do Prompts Really Prompt? Exploring the Prompt Understanding Capability of Whisper](https://mlsanity.com/paper/2406.05806/) (2024)
- [DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition](https://mlsanity.com/paper/2403.17645/) (2024)
- [Hearing More with Less: Multi-Modal Retrieval-and-Selection Augmented Conversational LLM-Based ASR](https://mlsanity.com/paper/2508.01166/) (2025)
- [PROFASR-BENCH: A Benchmark for Context-Conditioned ASR in High-Stakes Professional Speech](https://mlsanity.com/paper/2512.23686/) (2025)
- [IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages](https://mlsanity.com/paper/2606.19157/) (2026)

## Abstract (arXiv)

Contextual ASR can benefit from semantic cues or from target words explicitly provided in the context. We introduce HearInContext, a Mandarin-English benchmark that pairs shared synthetic speech with assistant replies supporting different interpretations. The benchmark comprises 3,764 semantic test cases built around homophones. Implicit contexts exclude candidate words; explicit contexts name the target. No-context and unrelated-context controls measure the benefit of relevant history and sensitivity to irrelevant history. Context-capable models benefit from implicit cues but achieve higher target recall with explicit hints. Fine-tuning Qwen3-ASR-1.7B improves implicit-context target recall by 11.4 percentage points in both Mandarin and English, while absolute CER/WER changes on AISHELL-1 and LibriSpeech remain below 0.1 percentage points. Gains extend to explicit conditions excluded from fine-tuning and to Mandarin hotword recognition on real recordings. Code and data are available at https://github.com/OPPO-Mente-Lab/HearInContext