Paper Info
Title
Using Artistic Markers and Speaker Identification for Narrative-Theme Navigation of Seinfeld Episodes
Abstract
This article describes a system to navigate Seinfeld episodes based on acoustic event detection and speaker identification of the audio track and subsequent inference of narrative themes based on genre-specific production rules. The system distinguishes laughter, music, and other noise as well as speech segments. Speech segments are then identified against pre-trained speaker models. Given this segmentation and the artistic production rules that underlie the ``situation comedy'' genre and Seinfeld in particular, the system enables a user to browse an episode by scene, punchline, and dialog segments. The themes can be filtered by the main actors, e.g. the user can choose to see only punchlines by Jerry and Kramer. Based on the length of the laughter, the top-5 punchlines are identified and presented to the user. The segmentation is then presented in an Applet-based graphical video browser that is intended to extend a typical YouTube videoplayer.
Year
DOI
Venue
2009
10.1109/ISM.2009.20
ISM
Keywords
Field
DocType
narrative-theme navigation,pre-trained speaker model,speech segment,speaker identification,top-5 punchlines,system distinguishes laughter,artistic markers,artistic production rule,seinfeld episodes,seinfeld episode,applet-based graphical video browser,genre-specific production rule,acoustic event detection,tv,acoustics,speech,data mining,navigation,speech segmentation,production,speaker recognition
Laughter,Dialog box,Computer vision,Comedy,Computer science,Inference,Segmentation,Narrative,Speech recognition,Speaker recognition,Artificial intelligence,Java applet
Conference
Citations 
PageRank 
References 
3
0.49
11
Authors
3
Name
Order
Citations
PageRank
Gerald Friedland1112796.23
Luke Gottlieb2615.79
Adam Janin325034.11