Abstract
Automatic detection of misannotated words in single-speaker read-speech corpora is investigated in this paper. Support vector machine (SVM) classifier was proposed to detect the misannotated words. Its performance was evaluated with respect to various word-level feature sets. The SVM classifier was shown to perform very well with both high precision and recall scores and with F1 measure being almost 88%. This is a statistically significant improvement over a traditionally used outlier-based detection method.
The work has been supported by the Technology Agency of the Czech Republic, project No. TA01030476, and by the European Regional Development Fund (ERDF), project “New Technologies for Information Society” (NTIS), European Centre of Excellence, ED1.1.00/02.0090. The access to the MetaCentrum clusters provided under the programme LM2010005 is highly appreciated.
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Matoušek, J., Tihelka, D. (2013). SVM-Based Detection of Misannotated Words in Read Speech Corpora. In: Habernal, I., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2013. Lecture Notes in Computer Science(), vol 8082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40585-3_58
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DOI: https://doi.org/10.1007/978-3-642-40585-3_58
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