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Wed 18 March
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Wednesday 18 March 2020 16.30 - 18.30
R-4 ORA15 Doing Oral History after the Digital Turn. Challenges & Opportunities
Lipsius, 307
Network: Oral History Chairs: -
Organizers: Norah Karrouche, Stefania Scagliola Discussants: -
Luise Corti, Jeannine Beeken : Annotating and Interpreting Oral History Interviews in a Digital Environment
Annotation, linguistic and emotion recognition tools can cater to the needs of oral historians who are interested in performing content, discourse or narrative analyses of transcripts and/or audio/video of interview collections. This paper focuses on the ways in which proprietary software designed with social scientists in mind, and open source ... (Show more)
Annotation, linguistic and emotion recognition tools can cater to the needs of oral historians who are interested in performing content, discourse or narrative analyses of transcripts and/or audio/video of interview collections. This paper focuses on the ways in which proprietary software designed with social scientists in mind, and open source tools favoured by linguists might be of use to oral historians. Annotation tools can assist an oral historian’s interpretation of interviews, for instance by performing so-called word frequency analyses on transcripts, and visualizing results in world clouds and pie charts. Linguistic tools in particular are able to analyze relations between words and any ties these relations might have to interviewer and interviewee. The ability to annotate and analyze the audio aspects of an oral history interview are of particular interest to this paper. When we read an oral history, but do not listen to it, we are missing out on emotions that may underpin the conversation. Studying social sign processing with the help of emotion recognition tools opens up the option of re-interpreting an interview, by reflecting on the function of the silence, or tone. The paper concludes by challenging to translate these insights into the paradigm the oral historian, and to widen their methodological perspective in data analysis. (Show less)

Stefania Scagliola : Oral History, Related Disciplines and how Technology can Support them
The oral history landscape consists of interviews conducted by individual scholars to answer a particular research question, or collections created as an archival effort to document a historical topic in a broader sense. Until the digital turn, the idea of reuse, of sharing, and of exploiting the multimodal character ... (Show more)
The oral history landscape consists of interviews conducted by individual scholars to answer a particular research question, or collections created as an archival effort to document a historical topic in a broader sense. Until the digital turn, the idea of reuse, of sharing, and of exploiting the multimodal character of interview data - the text, the voice, the emotions, the bodily movements - seemed principles that belonged to separate sub-disciplines. At present this mono-disciplinary approach can be challenged. This paper will first reflect on the variety of traditions in creating oral history data, and then present an outline of related disciplines and their methodological practices with regard to dealing with interview data. Presenting these landscapes of practices, within history, ethnography, social science and linguistics, is necessary to illustrate how digital technology can support existing practices and help develop cross disciplinary new ones. (Show less)

Arjan van Hessen, Christoph Draxler : Using Automatic Speech Recognition for Oral History Interviews
Automatic Speech Recognition, or ASR, is software that enables recordings of spoken word (audio or video) to be automatically converted into text. ASR can be applied in many ways. In the context of oral history, ASR is often considered as a way to substitute the classical cumbersome process of transcription. ... (Show more)
Automatic Speech Recognition, or ASR, is software that enables recordings of spoken word (audio or video) to be automatically converted into text. ASR can be applied in many ways. In the context of oral history, ASR is often considered as a way to substitute the classical cumbersome process of transcription. This paper will first elaborate on the applicability of ASR for oral history and life story interviewers. If the audio is of good quality and a 100% verbatim transcription of the spoken speech is not needed , ASR without the need for subsequent manual correction may suit the needs of the project, for instance when the generated speech is needed to be able to make the collection searchable. In other cases, when the audio quality is poor, the narrators speak in dialect and a verbatim transcription is necessary for a thorough analysis of the dialogue then manual transcription aided by the possibility to align the ‘dirty transcript’ with the audio a (forced alignment) may be a better option. This paper will try to answer the questions: how can ASR aid oral historians in the process of transcribing? And how can ASR also improve our ability to identify relevant excerpts in large collections of reco (Show less)



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