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Early error detection on word level
KTH, Superseded Departments, Speech, Music and Hearing.ORCID iD: 0000-0002-8579-1790
KTH, Superseded Departments, Speech, Music and Hearing.ORCID iD: 0000-0001-9327-9482
2004 (English)In: Proceedings of ISCA Tutorial and Research Workshop (ITRW) on Robustness Issues in Conversational Interaction, 2004Conference paper, Published paper (Refereed)
Abstract [en]

In this paper two studies are presented in which the detection of speech recognition errors on the word level was examined. In the first study, memory-based and transformation-based machine learning was used for the task, using confidence, lexical, contextual and discourse features. In the second study, we investigated which factors humans benefit from when detecting errors. Information from the speech recogniser (i.e. word confidence scores and 5-best lists) and contextual information were the factors investigated. The results show that word confidence scores are useful and that lexical and contextual (both from the utterance and from the discourse) features further improve performance.

Place, publisher, year, edition, pages
2004.
National Category
Computer Science Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-51787OAI: oai:DiVA.org:kth-51787DiVA: diva2:465082
Conference
ISCA Tutorial and Research Workshop (ITRW) on Robustness Issues in Conversational Interaction. Anglia, Norwich, UK. August 30-31, 2004
Note
tmh_import_11_12_14. QC 20111219Available from: 2011-12-14 Created: 2011-12-14 Last updated: 2011-12-19Bibliographically approved

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http://www.isca-speech.org/archive_open/archive_papers/robust2004/rob4_17.pdf

Authority records BETA

Skantze, GabrielEdlund, Jens

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  • en-US
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Output format
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