A Scalable Video Search Engine Based on Audio Content Indexing and Topic Segmentation

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 organisations, (iii) perform cross lingual search of news broadcasts, (iv) search for news through a map interface, (v) browse news by trending topics, and (vi) see automatically-generated textual clues for news segments, before listening. Our publicly searchable demonstrator currently indexes daily broadcast news content from 50 sources in English, French, Chinese, Arabic, Spanish, Dutch and Russian.