Paper Info
Title
Transcription Methods for Consistency, Volume and Efficiency
Abstract
This paper describes recent efforts at Linguistic Data Consortium at the University of Pennsylvania to create manual transcripts as a shared resource for human language technology research and evaluation. Speech recognition and related technologies in particular call for substantial volumes of transcribed speech for use in system development, and for human gold standard references for evaluating performance over time. Over the past several years LDC has developed a number of transcription approaches to support the varied goals of speech technology evaluation programs in multiple languages and genres. We describe each transcription method in detail, and report on the results of a comparative analysis of transcriber consistency and efficiency, for two transcription methods in three languages and five genres. Our findings suggest that transcripts for planned speech are generally more consistent than those for spontaneous speech, and that careful transcription methods result in higher rates of agreement when compared to quick transcription methods. We conclude with a general discussion of factors contributing to transcription quality, efficiency and consistency.
Year
Venue
Keywords
2010
LREC 2010 - SEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
gold standard,human language technology,comparative analysis,speech recognition
Field
DocType
Citations 
Speech corpus,Linguistic Data Consortium,Computer science,Speech recognition,Artificial intelligence,Natural language processing,System development,Shared resource,Language technology,Speech technology
Conference
4
PageRank 
References 
Authors
0.44
6
6
Name
Order
Citations
PageRank
Meghan Lammie Glenn1174.77
Stephanie Strassel251258.41
Haejoong Lee310523.68
Kazuaki Maeda413834.69
Ramez Zakhary540.44
Xuansong Li6729.93