Title
Lightly supervised alignment of subtitles on multi-genre broadcasts.
Abstract
This paper describes a system for performing alignment of subtitles to audio on multigenre broadcasts using a lightly supervised approach. Accurate alignment of subtitles plays a substantial role in the daily work of media companies and currently still requires large human effort. Here, a comprehensive approach to performing this task in an automated way using lightly supervised alignment is proposed. The paper explores the different alternatives to speech segmentation, lightly supervised speech recognition and alignment of text streams. The proposed system uses lightly supervised decoding to improve the alignment accuracy by performing language model adaptation using the target subtitles. The system thus built achieves the third best reported result in the alignment of broadcast subtitles in the Multi–Genre Broadcast (MGB) challenge, with an F1 score of 88.8%. This system is available for research and other non–commercial purposes through webASR, the University of Sheffield’s cloud–based speech technology web service. Taking as inputs an audio file and untimed subtitles, webASR can produce timed subtitles in multiple formats, including TTML, WebVTT and SRT.
Year
DOI
Venue
2018
10.1007/s11042-018-6050-1
Multimedia Tools Appl.
Keywords
Field
DocType
Multigenre broadcasts, Lightly supervised alignment, Language model adaptation, Subtitles
Computer vision,Broadcasting,F1 score,Computer science,WebVTT,Speech recognition,Artificial intelligence,Web service,Speech segmentation,Speech technology,Language model,Cloud computing
Journal
Volume
Issue
ISSN
77
23
1380-7501
Citations 
PageRank 
References 
0
0.34
20
Authors
9
Name
Order
Citations
PageRank
Oscar Saz114216.30
Salil Deena2273.61
Mortaza Doulaty3335.35
Madina Hasan4135.35
Bilal Khaliq500.34
Rosanna Milner6112.59
Raymond W. M. Ng741.10
Julia Olcoz871.57
Thomas Hain910514.91