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Multi-Modal automatic speech recognition (ASR) techniques aim to leverageadditional 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, whichprovide rich domain-specific information in the form of text and images, werelease SlideSpeech, a large-scale audio-visual corpus enriched with slides.The corpus contains 1,705 videos, 1,000+ hours, with 473 hours of high-qualitytranscribed speech. Moreover, the corpus contains a significant amount ofreal-time synchronized slides. In this work, we present the pipeline forconstructing the corpus and propose baseline methods for utilizing textinformation in the visual slide context. Through the application of keywordextraction and contextual ASR methods in the benchmark system, we demonstratethe potential of improving speech recognition performance by incorporatingtextual information from supplementary video slides.

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