Paper Info
Title
Semantic similarity for detecting recognition errors in automatic speech transcripts
Abstract
Browsing through large volumes of spoken audio is known to be a challenging task for end users. One way to alleviate this problem is to allow users to gist a spoken audio document by glancing over a transcript generated through Automatic Speech Recognition. Unfortunately, such transcripts typically contain many recognition errors which are highly distracting and make gisting more difficult. In this paper we present an approach that detects recognition errors by identifying words which are semantic outliers with respect to other words in the transcript. We describe several variants of this approach. We investigate a wide range of evaluation measures and we show that we can significantly reduce the number of errors in content words, with the trade-off of losing some good content words.
Year
DOI
Venue
2005
10.3115/1220575.1220582
HLT/EMNLP
Keywords
Field
DocType
semantic similarity
Semantic similarity,Automatic speech,End user,Computer science,Outlier,Speech recognition,Natural language processing,Artificial intelligence
Conference
Citations 
PageRank 
References 
23
1.89
23
Authors
2
Name
Order
Citations
PageRank
Diana Inkpen1105987.92
Alain Désilets213615.99