# SlideSpeech: A Large-Scale Slide-Enriched Audio-Visual Corpus

arXiv: https://arxiv.org/abs/2309.05396 · HTML: https://mlsanity.com/paper/2309.05396/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2023.

## Built on by (most cited first, 19 of 19)

- [Ming-Omni: A Unified Multimodal Model for Perception and Generation](https://mlsanity.com/paper/2506.09344/) (2025)
- [M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performance](https://mlsanity.com/paper/2502.18778/) (2025)
- [Ming-UniAudio: Speech LLM for Joint Understanding, Generation and Editing with Unified Representation](https://mlsanity.com/paper/2511.05516/) (2025)
- [ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark](https://mlsanity.com/paper/2507.05727/) (2025)
- [SLAM-LLM: A Modular, Open-Source Multimodal Large Language Model Framework and Best Practice for Speech, Language, Audio and Music Processing](https://mlsanity.com/paper/2601.09385/) (2026)
- [Text-only adaptation in LLM-based ASR through text denoising](https://mlsanity.com/paper/2601.20900/) (2026)
- [Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning](https://mlsanity.com/paper/2506.05671/) (2025)
- [Chinese-LiPS: A Chinese audio-visual speech recognition dataset with Lip-reading and Presentation Slides](https://mlsanity.com/paper/2504.15066/) (2025)
- [Do Slides Help? Multi-modal Context for Automatic Transcription of Conference Talks](https://mlsanity.com/paper/2510.13979/) (2025)
- [M3AV: A Multimodal, Multigenre, and Multipurpose Audio-Visual Academic Lecture Dataset](https://mlsanity.com/paper/2403.14168/) (2024)
- [Multi-modal Speech Transformer Decoders: When Do Multiple Modalities Improve Accuracy?](https://mlsanity.com/paper/2409.09221/) (2024)
- [DocVideoQA: Towards Comprehensive Understanding of Document-Centric Videos through Question Answering](https://mlsanity.com/paper/2503.15887/) (2025)
- [Improving Contextual ASR via Multi-grained Fusion with Large Language Models](https://mlsanity.com/paper/2507.12252/) (2025)
- [OCR-Enhanced Multimodal ASR Can Read While Listening](https://mlsanity.com/paper/2601.18393/) (2026)
- [Closing the Speech-Text Gap with Limited Audio for Effective Domain Adaptation in LLM-Based ASR](https://mlsanity.com/paper/2604.06487/) (2026)
- [TASU2: Controllable CTC Simulation for Alignment and Low-Resource Adaptation of Speech LLMs](https://mlsanity.com/paper/2604.08384/) (2026)
- [HearInContext: A Benchmark for Implicit Context in Speech Recognition](https://mlsanity.com/paper/2609.18680/) (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)

Multi-Modal automatic speech recognition (ASR) techniques aim to leverage additional modalities to improve the performance of speech recognition systems. While existing approaches primarily focus on video or contextual information, the utilization of extra supplementary textual information has been overlooked. Recognizing the abundance of online conference videos with slides, which provide rich domain-specific information in the form of text and images, we release SlideSpeech, a large-scale audio-visual corpus enriched with slides. The corpus contains 1,705 videos, 1,000+ hours, with 473 hours of high-quality transcribed speech. Moreover, the corpus contains a significant amount of real-time synchronized slides. In this work, we present the pipeline for constructing the corpus and propose baseline methods for utilizing text information in the visual slide context. Through the application of keyword extraction and contextual ASR methods in the benchmark system, we demonstrate the potential of improving speech recognition performance by incorporating textual information from supplementary video slides.