Assessing the Performance of Automatic Speech Recognition Systems When Used by Native and Non-Native Speakers of Three Major Languages in Dictation Workflows

Julián Zapata, Andreas Søeborg Kirkedal

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    In this paper, we report on a two-part experiment aiming to assess and compare the performance of two types of automatic speech recognition (ASR) systems on two different computational platforms when used to augment dictation workflows. The experiment was performed with a sample of speakers of three major languages and with different linguistic profiles: non-native English speakers; non-native French speakers; and native Spanish speakers. The main objective of this experiment is to examine ASR performance in translation dictation (TD) and medical dictation (MD) workflows without manual transcription vs. with transcription. We discuss the advantages and drawbacks of a particular ASR approach in different computational platforms when used by various speakers of a given language, who may have different accents and levels of proficiency in that language, and who may have different levels of competence and experience dictating large volumes of text, and with ASR technology. Lastly, we enumerate several areas for future research.
    Original languageEnglish
    Title of host publicationProceedings of the 20th Nordic Conference of Computational Linguistics
    EditorsBeáta Megyesi
    Place of PublicationLinköping
    PublisherLinköping University Electronic Press
    Publication date2015
    ISBN (Print)9789175190983
    Publication statusPublished - 2015
    EventNODALIDA 2015: The 20th Nordic Conference of Computational Linguistics - Vilnius, Lithuania
    Duration: 11 May 201513 May 2015
    Conference number: 20


    ConferenceNODALIDA 2015: The 20th Nordic Conference of Computational Linguistics
    Internet address
    SeriesNEALT (Northern European Association of Language Technology) Proceedings Series

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