Abstract
Detecting filled pause in spontaneous speech recognition is very important since most of the speech is spontaneous and the most frequent phenomenon in Indonesian spontaneous speech is filled pause. This paper discusses the detection of filled pauses in spontaneous speech of Indonesian by utilizing acoustic features of the speech signal. The detection was conducted by employing statistical method using Naïve Bayes, Classification Tree, and Multilayer Perceptron algorithm. To build the model, speech data were collected from an entertainment program. Word parts in the data were labeled and its features were extracted. These include the formant and pitch stability, energy-drop, and duration. Half an hour of sentences contains 295 filled pause and 2082 non-filled pause words were employed as training data. Using 25 sentences as testing data, Naïve Bayes gave best detection correctness, 74.35 % on a closed data set and 71.43 % on an open data set.
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Sani, A., Lestari, D.P., Purwarianti, A. (2016). Filled Pause Detection in Indonesian Spontaneous Speech. In: Hasida, K., Purwarianti, A. (eds) Computational Linguistics. PACLING 2015. Communications in Computer and Information Science, vol 593. Springer, Singapore. https://doi.org/10.1007/978-981-10-0515-2_4
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DOI: https://doi.org/10.1007/978-981-10-0515-2_4
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