Abstract
In this research different clustering techniques are applied for grouping transcribed textual documents obtained out of audio streams. Since audio transcripts are normally highly erroneous, it is essential to reduce the negative impact of errors gained at the speech recognition stage. In attempt to overcome some of these errors, different stemming techniques are applied on the transcribed text. The goal of this research is to achieve automatic topic clustering of transcribed speech documents, and investigate the impact of applying stemming techniques in combination with a Chi-square similarity measure on the accuracy of the selected clustering algorithms. The evaluation—using F-Measure—showed that using root-based stemming in combination of spectral clustering technique achieved the highest accuracy.
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Jafar, A.A., Fakhr, M.W., Farouk, M.H. (2015). Clustering-Based Topic Identification of Transcribed Arabic Broadcast News. In: Elleithy, K., Sobh, T. (eds) New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering. Lecture Notes in Electrical Engineering, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-319-06764-3_32
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DOI: https://doi.org/10.1007/978-3-319-06764-3_32
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