A Scalable Video Search Engine Based on Audio Content Indexing and Topic Segmentation
Julien Lawto, Jean-Luc Gauvain (LIMSI), Lori Lamel (LIMSI), Gregory, Grefenstete, Guillaume Gravier (INRIA - IRISA), Julien Despres, Camille, Guinaudeau (INRIA - IRISA), Pascale S\'ebillot (INRIA - IRISA)

TL;DR
This paper introduces a scalable video search engine that uses audio content indexing and topic segmentation, enabling quick, focused access to relevant news broadcast segments across multiple languages and interfaces.
Contribution
It presents a novel system leveraging speech recognition and NLP for efficient, multilingual, and cross-lingual news broadcast segmentation and search, with a user-friendly interface.
Findings
Indexes 50 news sources in multiple languages
Enables cross-lingual and map-based search
Provides automatic textual clues for segments
Abstract
One important class of online videos is that of news broadcasts. Most news organisations provide near-immediate access to topical news broadcasts over the Internet, through RSS streams or podcasts. Until lately, technology has not made it possible for a user to automatically go to the smaller parts, within a longer broadcast, that might interest them. Recent advances in both speech recognition systems and natural language processing have led to a number of robust tools that allow us to provide users with quicker, more focussed access to relevant segments of one or more news broadcast videos. Here we present our new interface for browsing or searching news broadcasts (video/audio) that exploits these new language processing tools to (i) provide immediate access to topical passages within news broadcasts, (ii) browse news broadcasts by events as well as by people, places and…
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Taxonomy
TopicsVideo Analysis and Summarization · Music and Audio Processing · Speech Recognition and Synthesis
