Error analysis of automatic speech recognition using principal direction divisive partitioning

David McKoskey, Daniel Boley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


This paper describes an experiment performed using the Principal Direction Divisive Partitioning algorithm (Boley, 1998) in order to extract linguistic word error regularities from several sets of medical dictation data. For each of six physicians, two hundred finished medical dictations aligned with their corresponding automatic speech recognition output were clustered and the results analyzed for linguistic regularities between and within clusters. Sparsity measures indicated a good fit between the algorithm and the input data. Linguistic analysis of the output clusters showed evidence of systematic word recognition error for short words, function words, words with destressed vowels, and phonological confusion errors due to telephony (recording) bandwidth interference. No qualitatively significant distinctions between clusters could be made by examining word errors alone, but the results confirmed several informally held hypotheses and suggested several avenues of further investigation, such as the examination of word error contexts.

Original languageEnglish (US)
Title of host publicationMachine Learning
Subtitle of host publicationECML 2000 - 11th European Conference on Machine Learning, Proceedings
EditorsRamon Lopez de Mantaras, Enric Plaza
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783540451648
StatePublished - 2000
Event11th European Conference on Machine Learning, ECML 2000 - Barcelona, Catalonia, Spain
Duration: May 31 2000Jun 2 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other11th European Conference on Machine Learning, ECML 2000
CityBarcelona, Catalonia

Bibliographical note

Funding Information:
This work was partially supported by NSF grant IIS-9811229.

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.


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