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. © 2013 Springer-Verlag.
CITATION STYLE
Matoušek, J., & Tihelka, D. (2013). SVM-based detection of misannotated words in read speech corpora. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8082 LNAI, pp. 457–464). https://doi.org/10.1007/978-3-642-40585-3_58
Mendeley helps you to discover research relevant for your work.