# Do Slides Help? Multi-modal Context for Automatic Transcription of Conference Talks

arXiv: https://arxiv.org/abs/2510.13979 · HTML: https://mlsanity.com/paper/2510.13979/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://mlsanity.com/paper/2305.13245/) (2023)
- [Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs](https://mlsanity.com/paper/2503.01743/) (2025)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task Generalization](https://mlsanity.com/paper/2305.11095/) (2023)
- [SlideSpeech: A Large-Scale Slide-Enriched Audio-Visual Corpus](https://mlsanity.com/paper/2309.05396/) (2023)
- [Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition](https://mlsanity.com/paper/2207.00857/) (2022)
- [Adapting an ASR Foundation Model for Spoken Language Assessment](https://mlsanity.com/paper/2307.09378/) (2023)
- [Improving Multimodal Speech Recognition by Data Augmentation and Speech Representations](https://mlsanity.com/paper/2204.13206/) (2022)
- [Visual Features for Context-Aware Speech Recognition](https://mlsanity.com/paper/1712.00489/) (2017)
- [Visual Information Matters for ASR Error Correction](https://mlsanity.com/paper/2303.10160/) (2023)

## Built on by (most cited first, 5 of 5)

- [Multilingual Long-Form Speech Instruction Following: KIT's Submission to IWSLT 2026](https://mlsanity.com/paper/2606.04730/) (2026)
- [OmniFusion: Simultaneous Multilingual Multimodal Translations via Modular Fusion](https://mlsanity.com/paper/2512.00234/) (2025)
- [MUSCAT: MUltilingual, SCientific ConversATion Benchmark](https://mlsanity.com/paper/2604.15929/) (2026)
- [The Spoken Wikipedia Presentation Corpus](https://mlsanity.com/paper/2609.21676/) (2026)
- [ScreenASR-Bench: A Tiered Benchmark for Speech Recognition with Visual Context](https://mlsanity.com/paper/2610.16260/) (2026)

## Abstract (arXiv)

State-of-the-art (SOTA) Automatic Speech Recognition (ASR) systems primarily rely on acoustic information while disregarding additional multi-modal context. However, visual information are essential in disambiguation and adaptation. While most work focus on speaker images to handle noise conditions, this work also focuses on integrating presentation slides for the use cases of scientific presentation. In a first step, we create a benchmark for multi-modal presentation including an automatic analysis of transcribing domain-specific terminology. Next, we explore methods for augmenting speech models with multi-modal information. We mitigate the lack of datasets with accompanying slides by a suitable approach of data augmentation. Finally, we train a model using the augmented dataset, resulting in a relative reduction in word error rate of approximately 34%, across all words and 35%, for domain-specific terms compared to the baseline model.