Testing the Validity of Automatic Speech Recognition for Political Text Analysis
Christopher Wratil and
Political Analysis, 2019, vol. 27, issue 3, 339-359
The analysis of political texts from parliamentary speeches, party manifestos, social media, or press releases forms the basis of major and growing fields in political science, not least since advances in â€œtext-as-dataâ€ methods have rendered the analysis of large text corpora straightforward. However, a lot of sources of political speech are not regularly transcribed, and their on-demand transcription by humans is prohibitively expensive for research purposes. This class includes political speech in certain legislatures, during political party conferences as well as television interviews and talk shows. We showcase how scholars can use automatic speech recognition systems to analyze such speech with quantitative text analysis models of the â€œbag-of-wordsâ€ variety. To probe results for robustness to transcription error, we present an original â€œword error rate simulationâ€ (WERSIM) procedure implemented in $R$. We demonstrate the potential of automatic speech recognition to address open questions in political science with two substantive applications and discuss its limitations and practical challenges.
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Persistent link: https://EconPapers.repec.org/RePEc:cup:polals:v:27:y:2019:i:03:p:339-359_00
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