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SSHOC Webinar: CLARIN Hands-on Tutorial on Transcribing Interview Data

Henk van den Heuvel; Christoph Draxler


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    "description": "<p><strong>A high-quality orthographic transcript is the basis for all types of analyses of spoken language data. However, transcribing speech is a time-consuming and tedious task. But automatic speech recognition as well as NLP and text annotation tools can make this task much quicker and save you a lot of time and frustration.</strong></p>\n\n<p>In this first of a series of&nbsp;<a href=\"https://www.sshopencloud.eu/\">SSHOC</a>&nbsp;webinars,&nbsp;organised by the consortium partner&nbsp;<a href=\"https://www.clarin.eu/\"><strong>CLARIN ERIC,</strong></a>&nbsp;we will discuss the theoretical basis and the technology available for transcribing spoken language. In particular, we will focus on the role of automatic speech recognition &ndash; what are the opportunities, what are the pitfalls and to where can it be applied successfully.</p>\n\n<p><strong>ABOUT THE WEBINAR</strong></p>\n\n<p><em><strong>Introduction to working with interview data.</strong></em>&nbsp;Henk van den Heuvel&nbsp;- Head of the Humanities Lab at the Faculty of Arts, Radbound University Nijmegen - will briefly introduce the topic of using interviews as research instrument, and the cross-disciplinary nature of using interview data. He will also give some background on the Transcription Chain initiative which originated from oral history research but has a much larger potential.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>\n\n<p><em><strong>Demonstration of automatic transcription of speech.</strong></em>&nbsp;Christoph Draxler - Researcher at the Institute of Phonetics and Speech Processing at Ludwig Maximilian University Munich - will demonstrate the web portal for the automatic transcription of speech. This portal currently supports three languages (English, German, Dutch), with Italian and Czech in the pipeline. The portal provides a user-friendly interface, so that researchers without a technical background may use state-of-the-art recognizers, optimized annotation editors and powerful segmentation services to result in high-quality time-aligned transcripts. These transcripts are the basis for the following in-depth scientific analysis, e. g. topic modeling, linguistic structures, named entity recognition.</p>", 
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