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。现有方法主要关注视频或上下文信息，而额外辅助文本信息的利用尚未得到充分重视。鉴于线上会议视频及其配套幻灯片中蕴含丰富的领域特定文本与图像信息，我们发布了SlideSpeech——一种大规模幻灯片增强的视听语料库。该语料库包含1,705个视频、总计超1,000小时，其中473小时为高质量转录语音。此外，语料库还包含大量实时同步的幻灯片内容。本文展示了构建该语料库的技术流程，并提出了利用视觉幻灯片中文本信息的基准方法。通过关键词提取与上下文语音识别方法在基准系统中的应用，我们证明了融合视频幻灯片中的文本信息能够有效提升语音识别性能的潜力。