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.

翻译：多模态自动语音识别（ASR）技术旨在利用额外模态提升语音识别系统性能。现有方法主要聚焦于视频或上下文信息，而忽略了额外补充文本信息的利用。鉴于在线会议视频中大量存在的幻灯片资源（以文本和图像形式提供丰富的领域特定信息），我们发布了SlideSpeech——一个大规模幻灯片增强型音视频语料库。该语料库包含1,705个视频、总时长超1,000小时，其中473小时为高质量转录语音。此外，该语料库还包含大量实时同步的幻灯片。本文介绍了该语料库的构建流程，并提出了在视觉幻灯片上下文中利用文本信息的基线方法。通过在基准系统中应用关键词提取和上下文ASR方法，我们证明了从视频辅助幻灯片中整合文本信息可有效提升语音识别性能的潜力。